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Example of a Table of Evidence

• Evidence table

Assessing the evidence and Building a Table

• EBM-Assessing the Evidence, Critical Appraisal

One of the most important steps in writing a paper is showing the strength and rationale of the evidence you chosen.  The following document discusses the reasoning, grading and creation of a "Table of Evidence."  While table of evidences can differ, the examples given in this article are a great starting point.

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Construct an evidence table

Prior to extracting evidence, you should critically appraise the found articles. Determine for yourself how valid and accurate these materials are; only extract evidence from articles that meet your standards.

An evidence table is a common technique used to extract data to prepare systematic reviews, meta-analyses, and clinical guidelines. An evidence table for a literature review can be less rigorous and detailed than an evidence table used for these other types of publications.

In your table, each row represents one study. Each column describes one piece of information you will extract from each article. You will decide the column headings yourself and they will likely change for each literature review you conduct. Base the column headings on what information you need to extract. Usually, evidence tables for literature reviews will include 5-20 columns. See some sample evidence tables for various types of literature reviews:

An evidence table for a literature review where there is prospective research evidence in human subjects:

An evidence table for a literature review where there is prospective research evidence conducted in laboratories:

An evidence table for a literature review where there is more qualitative evidence:

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• Volume 19, Issue 1
• Reviewing the literature
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• Joanna Smith 1 ,
• Helen Noble 2
• 1 School of Healthcare, University of Leeds , Leeds , UK
• 2 School of Nursing and Midwifery, Queens's University Belfast , Belfast , UK
• Correspondence to Dr Joanna Smith , School of Healthcare, University of Leeds, Leeds LS2 9JT, UK; j.e.smith1{at}leeds.ac.uk

https://doi.org/10.1136/eb-2015-102252

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Implementing evidence into practice requires nurses to identify, critically appraise and synthesise research. This may require a comprehensive literature review: this article aims to outline the approaches and stages required and provides a working example of a published review.

Are there different approaches to undertaking a literature review?

What stages are required to undertake a literature review.

The rationale for the review should be established; consider why the review is important and relevant to patient care/safety or service delivery. For example, Noble et al 's 4 review sought to understand and make recommendations for practice and research in relation to dialysis refusal and withdrawal in patients with end-stage renal disease, an area of care previously poorly described. If appropriate, highlight relevant policies and theoretical perspectives that might guide the review. Once the key issues related to the topic, including the challenges encountered in clinical practice, have been identified formulate a clear question, and/or develop an aim and specific objectives. The type of review undertaken is influenced by the purpose of the review and resources available. However, the stages or methods used to undertake a review are similar across approaches and include:

Formulating clear inclusion and exclusion criteria, for example, patient groups, ages, conditions/treatments, sources of evidence/research designs;

Justifying data bases and years searched, and whether strategies including hand searching of journals, conference proceedings and research not indexed in data bases (grey literature) will be undertaken;

Developing search terms, the PICU (P: patient, problem or population; I: intervention; C: comparison; O: outcome) framework is a useful guide when developing search terms;

Developing search skills (eg, understanding Boolean Operators, in particular the use of AND/OR) and knowledge of how data bases index topics (eg, MeSH headings). Working with a librarian experienced in undertaking health searches is invaluable when developing a search.

Once studies are selected, the quality of the research/evidence requires evaluation. Using a quality appraisal tool, such as the Critical Appraisal Skills Programme (CASP) tools, 5 results in a structured approach to assessing the rigour of studies being reviewed. 3 Approaches to data synthesis for quantitative studies may include a meta-analysis (statistical analysis of data from multiple studies of similar designs that have addressed the same question), or findings can be reported descriptively. 6 Methods applicable for synthesising qualitative studies include meta-ethnography (themes and concepts from different studies are explored and brought together using approaches similar to qualitative data analysis methods), narrative summary, thematic analysis and content analysis. 7 Table 1 outlines the stages undertaken for a published review that summarised research about parents’ experiences of living with a child with a long-term condition. 8

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An example of rapid evidence assessment review

In summary, the type of literature review depends on the review purpose. For the novice reviewer undertaking a review can be a daunting and complex process; by following the stages outlined and being systematic a robust review is achievable. The importance of literature reviews should not be underestimated—they help summarise and make sense of an increasingly vast body of research promoting best evidence-based practice.

• ↵ Centre for Reviews and Dissemination . Guidance for undertaking reviews in health care . 3rd edn . York : CRD, York University , 2009 .
• ↵ Canadian Best Practices Portal. http://cbpp-pcpe.phac-aspc.gc.ca/interventions/selected-systematic-review-sites / ( accessed 7.8.2015 ).
• Bridges J , et al
• ↵ Critical Appraisal Skills Programme (CASP). http://www.casp-uk.net / ( accessed 7.8.2015 ).
• Dixon-Woods M ,
• Shaw R , et al
• Agarwal S ,
• Jones D , et al
• Cheater F ,

Twitter Follow Joanna Smith at @josmith175

Competing interests None declared.

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Cochrane Training

Chapter 14: completing ‘summary of findings’ tables and grading the certainty of the evidence.

Holger J Schünemann, Julian PT Higgins, Gunn E Vist, Paul Glasziou, Elie A Akl, Nicole Skoetz, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group (formerly Applicability and Recommendations Methods Group) and the Cochrane Statistical Methods Group

Key Points:

• A ‘Summary of findings’ table for a given comparison of interventions provides key information concerning the magnitudes of relative and absolute effects of the interventions examined, the amount of available evidence and the certainty (or quality) of available evidence.
• ‘Summary of findings’ tables include a row for each important outcome (up to a maximum of seven). Accepted formats of ‘Summary of findings’ tables and interactive ‘Summary of findings’ tables can be produced using GRADE’s software GRADEpro GDT.
• Cochrane has adopted the GRADE approach (Grading of Recommendations Assessment, Development and Evaluation) for assessing certainty (or quality) of a body of evidence.
• The GRADE approach specifies four levels of the certainty for a body of evidence for a given outcome: high, moderate, low and very low.
• GRADE assessments of certainty are determined through consideration of five domains: risk of bias, inconsistency, indirectness, imprecision and publication bias. For evidence from non-randomized studies and rarely randomized studies, assessments can then be upgraded through consideration of three further domains.

Cite this chapter as: Schünemann HJ, Higgins JPT, Vist GE, Glasziou P, Akl EA, Skoetz N, Guyatt GH. Chapter 14: Completing ‘Summary of findings’ tables and grading the certainty of the evidence. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook .

14.1 ‘Summary of findings’ tables

14.1.1 introduction to ‘summary of findings’ tables.

‘Summary of findings’ tables present the main findings of a review in a transparent, structured and simple tabular format. In particular, they provide key information concerning the certainty or quality of evidence (i.e. the confidence or certainty in the range of an effect estimate or an association), the magnitude of effect of the interventions examined, and the sum of available data on the main outcomes. Cochrane Reviews should incorporate ‘Summary of findings’ tables during planning and publication, and should have at least one key ‘Summary of findings’ table representing the most important comparisons. Some reviews may include more than one ‘Summary of findings’ table, for example if the review addresses more than one major comparison, or includes substantially different populations that require separate tables (e.g. because the effects differ or it is important to show results separately). In the Cochrane Database of Systematic Reviews (CDSR),  all ‘Summary of findings’ tables for a review appear at the beginning, before the Background section.

14.1.2 Selecting outcomes for ‘Summary of findings’ tables

Planning for the ‘Summary of findings’ table starts early in the systematic review, with the selection of the outcomes to be included in: (i) the review; and (ii) the ‘Summary of findings’ table. This is a crucial step, and one that review authors need to address carefully.

To ensure production of optimally useful information, Cochrane Reviews begin by developing a review question and by listing all main outcomes that are important to patients and other decision makers (see Chapter 2 and Chapter 3 ). The GRADE approach to assessing the certainty of the evidence (see Section 14.2 ) defines and operationalizes a rating process that helps separate outcomes into those that are critical, important or not important for decision making. Consultation and feedback on the review protocol, including from consumers and other decision makers, can enhance this process.

Critical outcomes are likely to include clearly important endpoints; typical examples include mortality and major morbidity (such as strokes and myocardial infarction). However, they may also represent frequent minor and rare major side effects, symptoms, quality of life, burdens associated with treatment, and resource issues (costs). Burdens represent the impact of healthcare workload on patient function and well-being, and include the demands of adhering to an intervention that patients or caregivers (e.g. family) may dislike, such as having to undergo more frequent tests, or the restrictions on lifestyle that certain interventions require (Spencer-Bonilla et al 2017).

Frequently, when formulating questions that include all patient-important outcomes for decision making, review authors will confront reports of studies that have not included all these outcomes. This is particularly true for adverse outcomes. For instance, randomized trials might contribute evidence on intended effects, and on frequent, relatively minor side effects, but not report on rare adverse outcomes such as suicide attempts. Chapter 19 discusses strategies for addressing adverse effects. To obtain data for all important outcomes it may be necessary to examine the results of non-randomized studies (see Chapter 24 ). Cochrane, in collaboration with others, has developed guidance for review authors to support their decision about when to look for and include non-randomized studies (Schünemann et al 2013).

If a review includes only randomized trials, these trials may not address all important outcomes and it may therefore not be possible to address these outcomes within the constraints of the review. Review authors should acknowledge these limitations and make them transparent to readers. Review authors are encouraged to include non-randomized studies to examine rare or long-term adverse effects that may not adequately be studied in randomized trials. This raises the possibility that harm outcomes may come from studies in which participants differ from those in studies used in the analysis of benefit. Review authors will then need to consider how much such differences are likely to impact on the findings, and this will influence the certainty of evidence because of concerns about indirectness related to the population (see Section 14.2.2 ).

Non-randomized studies can provide important information not only when randomized trials do not report on an outcome or randomized trials suffer from indirectness, but also when the evidence from randomized trials is rated as very low and non-randomized studies provide evidence of higher certainty. Further discussion of these issues appears also in Chapter 24 .

14.1.3 General template for ‘Summary of findings’ tables

Several alternative standard versions of ‘Summary of findings’ tables have been developed to ensure consistency and ease of use across reviews, inclusion of the most important information needed by decision makers, and optimal presentation (see examples at Figures 14.1.a and 14.1.b ). These formats are supported by research that focused on improved understanding of the information they intend to convey (Carrasco-Labra et al 2016, Langendam et al 2016, Santesso et al 2016). They are available through GRADE’s official software package developed to support the GRADE approach: GRADEpro GDT (www.gradepro.org).

Standard Cochrane ‘Summary of findings’ tables include the following elements using one of the accepted formats. Further guidance on each of these is provided in Section 14.1.6 .

• A brief description of the population and setting addressed by the available evidence (which may be slightly different to or narrower than those defined by the review question).
• A brief description of the comparison addressed in the ‘Summary of findings’ table, including both the experimental and comparison interventions.
• A list of the most critical and/or important health outcomes, both desirable and undesirable, limited to seven or fewer outcomes.
• A measure of the typical burden of each outcomes (e.g. illustrative risk, or illustrative mean, on comparator intervention).
• The absolute and relative magnitude of effect measured for each (if both are appropriate).
• The numbers of participants and studies contributing to the analysis of each outcomes.
• A GRADE assessment of the overall certainty of the body of evidence for each outcome (which may vary by outcome).
• Space for comments.
• Explanations (formerly known as footnotes).

Ideally, ‘Summary of findings’ tables are supported by more detailed tables (known as ‘evidence profiles’) to which the review may be linked, which provide more detailed explanations. Evidence profiles include the same important health outcomes, and provide greater detail than ‘Summary of findings’ tables of both of the individual considerations feeding into the grading of certainty and of the results of the studies (Guyatt et al 2011a). They ensure that a structured approach is used to rating the certainty of evidence. Although they are rarely published in Cochrane Reviews, evidence profiles are often used, for example, by guideline developers in considering the certainty of the evidence to support guideline recommendations. Review authors will find it easier to develop the ‘Summary of findings’ table by completing the rating of the certainty of evidence in the evidence profile first in GRADEpro GDT. They can then automatically convert this to one of the ‘Summary of findings’ formats in GRADEpro GDT, including an interactive ‘Summary of findings’ for publication.

As a measure of the magnitude of effect for dichotomous outcomes, the ‘Summary of findings’ table should provide a relative measure of effect (e.g. risk ratio, odds ratio, hazard) and measures of absolute risk. For other types of data, an absolute measure alone (such as a difference in means for continuous data) might be sufficient. It is important that the magnitude of effect is presented in a meaningful way, which may require some transformation of the result of a meta-analysis (see also Chapter 15, Section 15.4 and Section 15.5 ). Reviews with more than one main comparison should include a separate ‘Summary of findings’ table for each comparison.

Figure 14.1.a provides an example of a ‘Summary of findings’ table. Figure 15.1.b  provides an alternative format that may further facilitate users’ understanding and interpretation of the review’s findings. Evidence evaluating different formats suggests that the ‘Summary of findings’ table should include a risk difference as a measure of the absolute effect and authors should preferably use a format that includes a risk difference .

A detailed description of the contents of a ‘Summary of findings’ table appears in Section 14.1.6 .

Figure 14.1.a Example of a ‘Summary of findings’ table

Summary of findings (for interactive version click here )

a All the stockings in the nine studies included in this review were below-knee compression stockings. In four studies the compression strength was 20 mmHg to 30 mmHg at the ankle. It was 10 mmHg to 20 mmHg in the other four studies. Stockings come in different sizes. If a stocking is too tight around the knee it can prevent essential venous return causing the blood to pool around the knee. Compression stockings should be fitted properly. A stocking that is too tight could cut into the skin on a long flight and potentially cause ulceration and increased risk of DVT. Some stockings can be slightly thicker than normal leg covering and can be potentially restrictive with tight foot wear. It is a good idea to wear stockings around the house prior to travel to ensure a good, comfortable fit. Participants put their stockings on two to three hours before the flight in most of the studies. The availability and cost of stockings can vary.

b Two studies recruited high risk participants defined as those with previous episodes of DVT, coagulation disorders, severe obesity, limited mobility due to bone or joint problems, neoplastic disease within the previous two years, large varicose veins or, in one of the studies, participants taller than 190 cm and heavier than 90 kg. The incidence for the seven studies that excluded high risk participants was 1.45% and the incidence for the two studies that recruited high-risk participants (with at least one risk factor) was 2.43%. We have used 10 and 30 per 1000 to express different risk strata, respectively.

c The confidence interval crosses no difference and does not rule out a small increase.

d The measurement of oedema was not validated (indirectness of the outcome) or blinded to the intervention (risk of bias).

e If there are very few or no events and the number of participants is large, judgement about the certainty of evidence (particularly judgements about imprecision) may be based on the absolute effect. Here the certainty rating may be considered ‘high’ if the outcome was appropriately assessed and the event, in fact, did not occur in 2821 studied participants.

f None of the other studies reported adverse effects, apart from four cases of superficial vein thrombosis in varicose veins in the knee region that were compressed by the upper edge of the stocking in one study.

Figure 14.1.b Example of alternative ‘Summary of findings’ table

14.1.4 Producing ‘Summary of findings’ tables

The GRADE Working Group’s software, GRADEpro GDT ( www.gradepro.org ), including GRADE’s interactive handbook, is available to assist review authors in the preparation of ‘Summary of findings’ tables. GRADEpro can use data on the comparator group risk and the effect estimate (entered by the review authors or imported from files generated in RevMan) to produce the relative effects and absolute risks associated with experimental interventions. In addition, it leads the user through the process of a GRADE assessment, and produces a table that can be used as a standalone table in a review (including by direct import into software such as RevMan or integration with RevMan Web), or an interactive ‘Summary of findings’ table (see help resources in GRADEpro).

14.1.5 Statistical considerations in ‘Summary of findings’ tables

14.1.5.1 dichotomous outcomes.

‘Summary of findings’ tables should include both absolute and relative measures of effect for dichotomous outcomes. Risk ratios, odds ratios and risk differences are different ways of comparing two groups with dichotomous outcome data (see Chapter 6, Section 6.4.1 ). Furthermore, there are two distinct risk ratios, depending on which event (e.g. ‘yes’ or ‘no’) is the focus of the analysis (see Chapter 6, Section 6.4.1.5 ). In the presence of a non-zero intervention effect, any variation across studies in the comparator group risks (i.e. variation in the risk of the event occurring without the intervention of interest, for example in different populations) makes it impossible for more than one of these measures to be truly the same in every study.

It has long been assumed in epidemiology that relative measures of effect are more consistent than absolute measures of effect from one scenario to another. There is empirical evidence to support this assumption (Engels et al 2000, Deeks and Altman 2001, Furukawa et al 2002). For this reason, meta-analyses should generally use either a risk ratio or an odds ratio as a measure of effect (see Chapter 10, Section 10.4.3 ). Correspondingly, a single estimate of relative effect is likely to be a more appropriate summary than a single estimate of absolute effect. If a relative effect is indeed consistent across studies, then different comparator group risks will have different implications for absolute benefit. For instance, if the risk ratio is consistently 0.75, then the experimental intervention would reduce a comparator group risk of 80% to 60% in the intervention group (an absolute risk reduction of 20 percentage points), but would also reduce a comparator group risk of 20% to 15% in the intervention group (an absolute risk reduction of 5 percentage points).

‘Summary of findings’ tables are built around the assumption of a consistent relative effect. It is therefore important to consider the implications of this effect for different comparator group risks (these can be derived or estimated from a number of sources, see Section 14.1.6.3 ), which may require an assessment of the certainty of evidence for prognostic evidence (Spencer et al 2012, Iorio et al 2015). For any comparator group risk, it is possible to estimate a corresponding intervention group risk (i.e. the absolute risk with the intervention) from the meta-analytic risk ratio or odds ratio. Note that the numbers provided in the ‘Corresponding risk’ column are specific to the ‘risks’ in the adjacent column.

For the meta-analytic risk ratio (RR) and assumed comparator risk (ACR) the corresponding intervention risk is obtained as:

As an example, in Figure 14.1.a , the meta-analytic risk ratio for symptomless deep vein thrombosis (DVT) is RR = 0.10 (95% CI 0.04 to 0.26). Assuming a comparator risk of ACR = 10 per 1000 = 0.01, we obtain:

For the meta-analytic odds ratio (OR) and assumed comparator risk, ACR, the corresponding intervention risk is obtained as:

Upper and lower confidence limits for the corresponding intervention risk are obtained by replacing RR or OR by their upper and lower confidence limits, respectively (e.g. replacing 0.10 with 0.04, then with 0.26, in the example). Such confidence intervals do not incorporate uncertainty in the assumed comparator risks.

When dealing with risk ratios, it is critical that the same definition of ‘event’ is used as was used for the meta-analysis. For example, if the meta-analysis focused on ‘death’ (as opposed to survival) as the event, then corresponding risks in the ‘Summary of findings’ table must also refer to ‘death’.

In (rare) circumstances in which there is clear rationale to assume a consistent risk difference in the meta-analysis, in principle it is possible to present this for relevant ‘assumed risks’ and their corresponding risks, and to present the corresponding (different) relative effects for each assumed risk.

The risk difference expresses the difference between the ACR and the corresponding intervention risk (or the difference between the experimental and the comparator intervention).

For the meta-analytic risk ratio (RR) and assumed comparator risk (ACR) the corresponding risk difference is obtained as (note that risks can also be expressed using percentage or percentage points):

As an example, in Figure 14.1.b the meta-analytic risk ratio is 0.41 (95% CI 0.29 to 0.55) for diarrhoea in children less than 5 years of age. Assuming a comparator group risk of 22.3% we obtain:

For the meta-analytic odds ratio (OR) and assumed comparator risk (ACR) the absolute risk difference is obtained as (percentage points):

Upper and lower confidence limits for the absolute risk difference are obtained by re-running the calculation above while replacing RR or OR by their upper and lower confidence limits, respectively (e.g. replacing 0.41 with 0.28, then with 0.55, in the example). Such confidence intervals do not incorporate uncertainty in the assumed comparator risks.

14.1.5.2 Time-to-event outcomes

Time-to-event outcomes measure whether and when a particular event (e.g. death) occurs (van Dalen et al 2007). The impact of the experimental intervention relative to the comparison group on time-to-event outcomes is usually measured using a hazard ratio (HR) (see Chapter 6, Section 6.8.1 ).

A hazard ratio expresses a relative effect estimate. It may be used in various ways to obtain absolute risks and other interpretable quantities for a specific population. Here we describe how to re-express hazard ratios in terms of: (i) absolute risk of event-free survival within a particular period of time; (ii) absolute risk of an event within a particular period of time; and (iii) median time to the event. All methods are built on an assumption of consistent relative effects (i.e. that the hazard ratio does not vary over time).

(i) Absolute risk of event-free survival within a particular period of time Event-free survival (e.g. overall survival) is commonly reported by individual studies. To obtain absolute effects for time-to-event outcomes measured as event-free survival, the summary HR can be used in conjunction with an assumed proportion of patients who are event-free in the comparator group (Tierney et al 2007). This proportion of patients will be specific to a period of time of observation. However, it is not strictly necessary to specify this period of time. For instance, a proportion of 50% of event-free patients might apply to patients with a high event rate observed over 1 year, or to patients with a low event rate observed over 2 years.

As an example, suppose the meta-analytic hazard ratio is 0.42 (95% CI 0.25 to 0.72). Assuming a comparator group risk of event-free survival (e.g. for overall survival people being alive) at 2 years of ACR = 900 per 1000 = 0.9 we obtain:

so that that 956 per 1000 people will be alive with the experimental intervention at 2 years. The derivation of the risk should be explained in a comment or footnote.

(ii) Absolute risk of an event within a particular period of time To obtain this absolute effect, again the summary HR can be used (Tierney et al 2007):

In the example, suppose we assume a comparator group risk of events (e.g. for mortality, people being dead) at 2 years of ACR = 100 per 1000 = 0.1. We obtain:

so that that 44 per 1000 people will be dead with the experimental intervention at 2 years.

(iii) Median time to the event Instead of absolute numbers, the time to the event in the intervention and comparison groups can be expressed as median survival time in months or years. To obtain median survival time the pooled HR can be applied to an assumed median survival time in the comparator group (Tierney et al 2007):

In the example, assuming a comparator group median survival time of 80 months, we obtain:

For all three of these options for re-expressing results of time-to-event analyses, upper and lower confidence limits for the corresponding intervention risk are obtained by replacing HR by its upper and lower confidence limits, respectively (e.g. replacing 0.42 with 0.25, then with 0.72, in the example). Again, as for dichotomous outcomes, such confidence intervals do not incorporate uncertainty in the assumed comparator group risks. This is of special concern for long-term survival with a low or moderate mortality rate and a corresponding high number of censored patients (i.e. a low number of patients under risk and a high censoring rate).

14.1.6 Detailed contents of a ‘Summary of findings’ table

14.1.6.1 table title and header.

The title of each ‘Summary of findings’ table should specify the healthcare question, framed in terms of the population and making it clear exactly what comparison of interventions are made. In Figure 14.1.a , the population is people taking long aeroplane flights, the intervention is compression stockings, and the control is no compression stockings.

The first rows of each ‘Summary of findings’ table should provide the following ‘header’ information:

Patients or population This further clarifies the population (and possibly the subpopulations) of interest and ideally the magnitude of risk of the most crucial adverse outcome at which an intervention is directed. For instance, people on a long-haul flight may be at different risks for DVT; those using selective serotonin reuptake inhibitors (SSRIs) might be at different risk for side effects; while those with atrial fibrillation may be at low (< 1%), moderate (1% to 4%) or high (> 4%) yearly risk of stroke.

Setting This should state any specific characteristics of the settings of the healthcare question that might limit the applicability of the summary of findings to other settings (e.g. primary care in Europe and North America).

Intervention The experimental intervention.

Comparison The comparator intervention (including no specific intervention).

14.1.6.2 Outcomes

The rows of a ‘Summary of findings’ table should include all desirable and undesirable health outcomes (listed in order of importance) that are essential for decision making, up to a maximum of seven outcomes. If there are more outcomes in the review, review authors will need to omit the less important outcomes from the table, and the decision selecting which outcomes are critical or important to the review should be made during protocol development (see Chapter 3 ). Review authors should provide time frames for the measurement of the outcomes (e.g. 90 days or 12 months) and the type of instrument scores (e.g. ranging from 0 to 100).

Note that review authors should include the pre-specified critical and important outcomes in the table whether data are available or not. However, they should be alert to the possibility that the importance of an outcome (e.g. a serious adverse effect) may only become known after the protocol was written or the analysis was carried out, and should take appropriate actions to include these in the ‘Summary of findings’ table.

The ‘Summary of findings’ table can include effects in subgroups of the population for different comparator risks and effect sizes separately. For instance, in Figure 14.1.b effects are presented for children younger and older than 5 years separately. Review authors may also opt to produce separate ‘Summary of findings’ tables for different populations.

Review authors should include serious adverse events, but it might be possible to combine minor adverse events as a single outcome, and describe this in an explanatory footnote (note that it is not appropriate to add events together unless they are independent, that is, a participant who has experienced one adverse event has an unaffected chance of experiencing the other adverse event).

Outcomes measured at multiple time points represent a particular problem. In general, to keep the table simple, review authors should present multiple time points only for outcomes critical to decision making, where either the result or the decision made are likely to vary over time. The remainder should be presented at a common time point where possible.

Review authors can present continuous outcome measures in the ‘Summary of findings’ table and should endeavour to make these interpretable to the target audience. This requires that the units are clear and readily interpretable, for example, days of pain, or frequency of headache, and the name and scale of any measurement tools used should be stated (e.g. a Visual Analogue Scale, ranging from 0 to 100). However, many measurement instruments are not readily interpretable by non-specialist clinicians or patients, for example, points on a Beck Depression Inventory or quality of life score. For these, a more interpretable presentation might involve converting a continuous to a dichotomous outcome, such as >50% improvement (see Chapter 15, Section 15.5 ).

14.1.6.3 Best estimate of risk with comparator intervention

Review authors should provide up to three typical risks for participants receiving the comparator intervention. For dichotomous outcomes, we recommend that these be presented in the form of the number of people experiencing the event per 100 or 1000 people (natural frequency) depending on the frequency of the outcome. For continuous outcomes, this would be stated as a mean or median value of the outcome measured.

Estimated or assumed comparator intervention risks could be based on assessments of typical risks in different patient groups derived from the review itself, individual representative studies in the review, or risks derived from a systematic review of prognosis studies or other sources of evidence which may in turn require an assessment of the certainty for the prognostic evidence (Spencer et al 2012, Iorio et al 2015). Ideally, risks would reflect groups that clinicians can easily identify on the basis of their presenting features.

An explanatory footnote should specify the source or rationale for each comparator group risk, including the time period to which it corresponds where appropriate. In Figure 14.1.a , clinicians can easily differentiate individuals with risk factors for deep venous thrombosis from those without. If there is known to be little variation in baseline risk then review authors may use the median comparator group risk across studies. If typical risks are not known, an option is to choose the risk from the included studies, providing the second highest for a high and the second lowest for a low risk population.

14.1.6.4 Risk with intervention

For dichotomous outcomes, review authors should provide a corresponding absolute risk for each comparator group risk, along with a confidence interval. This absolute risk with the (experimental) intervention will usually be derived from the meta-analysis result presented in the relative effect column (see Section 14.1.6.6 ). Formulae are provided in Section 14.1.5 . Review authors should present the absolute effect in the same format as the risks with comparator intervention (see Section 14.1.6.3 ), for example as the number of people experiencing the event per 1000 people.

For continuous outcomes, a difference in means or standardized difference in means should be presented with its confidence interval. These will typically be obtained directly from a meta-analysis. Explanatory text should be used to clarify the meaning, as in Figures 14.1.a and 14.1.b .

14.1.6.5 Risk difference

For dichotomous outcomes, the risk difference can be provided using one of the ‘Summary of findings’ table formats as an additional option (see Figure 14.1.b ). This risk difference expresses the difference between the experimental and comparator intervention and will usually be derived from the meta-analysis result presented in the relative effect column (see Section 14.1.6.6 ). Formulae are provided in Section 14.1.5 . Review authors should present the risk difference in the same format as assumed and corresponding risks with comparator intervention (see Section 14.1.6.3 ); for example, as the number of people experiencing the event per 1000 people or as percentage points if the assumed and corresponding risks are expressed in percentage.

For continuous outcomes, if the ‘Summary of findings’ table includes this option, the mean difference can be presented here and the ‘corresponding risk’ column left blank (see Figure 14.1.b ).

14.1.6.6 Relative effect (95% CI)

The relative effect will typically be a risk ratio or odds ratio (or occasionally a hazard ratio) with its accompanying 95% confidence interval, obtained from a meta-analysis performed on the basis of the same effect measure. Risk ratios and odds ratios are similar when the comparator intervention risks are low and effects are small, but may differ considerably when comparator group risks increase. The meta-analysis may involve an assumption of either fixed or random effects, depending on what the review authors consider appropriate, and implying that the relative effect is either an estimate of the effect of the intervention, or an estimate of the average effect of the intervention across studies, respectively.

14.1.6.7 Number of participants (studies)

This column should include the number of participants assessed in the included studies for each outcome and the corresponding number of studies that contributed these participants.

14.1.6.8 Certainty of the evidence (GRADE)

Review authors should comment on the certainty of the evidence (also known as quality of the body of evidence or confidence in the effect estimates). Review authors should use the specific evidence grading system developed by the GRADE Working Group (Atkins et al 2004, Guyatt et al 2008, Guyatt et al 2011a), which is described in detail in Section 14.2 . The GRADE approach categorizes the certainty in a body of evidence as ‘high’, ‘moderate’, ‘low’ or ‘very low’ by outcome. This is a result of judgement, but the judgement process operates within a transparent structure. As an example, the certainty would be ‘high’ if the summary were of several randomized trials with low risk of bias, but the rating of certainty becomes lower if there are concerns about risk of bias, inconsistency, indirectness, imprecision or publication bias. Judgements other than of ‘high’ certainty should be made transparent using explanatory footnotes or the ‘Comments’ column in the ‘Summary of findings’ table (see Section 14.1.6.10 ).

14.1.6.9 Comments

The aim of the ‘Comments’ field is to help interpret the information or data identified in the row. For example, this may be on the validity of the outcome measure or the presence of variables that are associated with the magnitude of effect. Important caveats about the results should be flagged here. Not all rows will need comments, and it is best to leave a blank if there is nothing warranting a comment.

14.1.6.10 Explanations

Detailed explanations should be included as footnotes to support the judgements in the ‘Summary of findings’ table, such as the overall GRADE assessment. The explanations should describe the rationale for important aspects of the content. Table 14.1.a lists guidance for useful explanations. Explanations should be concise, informative, relevant, easy to understand and accurate. If explanations cannot be sufficiently described in footnotes, review authors should provide further details of the issues in the Results and Discussion sections of the review.

Table 14.1.a Guidance for providing useful explanations in ‘Summary of findings’ (SoF) tables. Adapted from Santesso et al (2016)

14.2 Assessing the certainty or quality of a body of evidence

14.2.1 the grade approach.

The Grades of Recommendation, Assessment, Development and Evaluation Working Group (GRADE Working Group) has developed a system for grading the certainty of evidence (Schünemann et al 2003, Atkins et al 2004, Schünemann et al 2006, Guyatt et al 2008, Guyatt et al 2011a). Over 100 organizations including the World Health Organization (WHO), the American College of Physicians, the American Society of Hematology (ASH), the Canadian Agency for Drugs and Technology in Health (CADTH) and the National Institutes of Health and Clinical Excellence (NICE) in the UK have adopted the GRADE system ( www.gradeworkinggroup.org ).

Cochrane has also formally adopted this approach, and all Cochrane Reviews should use GRADE to evaluate the certainty of evidence for important outcomes (see MECIR Box 14.2.a ).

MECIR Box 14.2.a Relevant expectations for conduct of intervention reviews

For systematic reviews, the GRADE approach defines the certainty of a body of evidence as the extent to which one can be confident that an estimate of effect or association is close to the quantity of specific interest. Assessing the certainty of a body of evidence involves consideration of within- and across-study risk of bias (limitations in study design and execution or methodological quality), inconsistency (or heterogeneity), indirectness of evidence, imprecision of the effect estimates and risk of publication bias (see Section 14.2.2 ), as well as domains that may increase our confidence in the effect estimate (as described in Section 14.2.3 ). The GRADE system entails an assessment of the certainty of a body of evidence for each individual outcome. Judgements about the domains that determine the certainty of evidence should be described in the results or discussion section and as part of the ‘Summary of findings’ table.

The GRADE approach specifies four levels of certainty ( Figure 14.2.a ). For interventions, including diagnostic and other tests that are evaluated as interventions (Schünemann et al 2008b, Schünemann et al 2008a, Balshem et al 2011, Schünemann et al 2012), the starting point for rating the certainty of evidence is categorized into two types:

• randomized trials; and
• non-randomized studies of interventions (NRSI), including observational studies (including but not limited to cohort studies, and case-control studies, cross-sectional studies, case series and case reports, although not all of these designs are usually included in Cochrane Reviews).

There are many instances in which review authors rely on information from NRSI, in particular to evaluate potential harms (see Chapter 24 ). In addition, review authors can obtain relevant data from both randomized trials and NRSI, with each type of evidence complementing the other (Schünemann et al 2013).

In GRADE, a body of evidence from randomized trials begins with a high-certainty rating while a body of evidence from NRSI begins with a low-certainty rating. The lower rating with NRSI is the result of the potential bias induced by the lack of randomization (i.e. confounding and selection bias).

However, when using the new Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool (Sterne et al 2016), an assessment tool that covers the risk of bias due to lack of randomization, all studies may start as high certainty of the evidence (Schünemann et al 2018). The approach of starting all study designs (including NRSI) as high certainty does not conflict with the initial GRADE approach of starting the rating of NRSI as low certainty evidence. This is because a body of evidence from NRSI should generally be downgraded by two levels due to the inherent risk of bias associated with the lack of randomization, namely confounding and selection bias. Not downgrading NRSI from high to low certainty needs transparent and detailed justification for what mitigates concerns about confounding and selection bias (Schünemann et al 2018). Very few examples of where not rating down by two levels is appropriate currently exist.

The highest certainty rating is a body of evidence when there are no concerns in any of the GRADE factors listed in Figure 14.2.a . Review authors often downgrade evidence to moderate, low or even very low certainty evidence, depending on the presence of the five factors in Figure 14.2.a . Usually, certainty rating will fall by one level for each factor, up to a maximum of three levels for all factors. If there are very severe problems for any one domain (e.g. when assessing risk of bias, all studies were unconcealed, unblinded and lost over 50% of their patients to follow-up), evidence may fall by two levels due to that factor alone. It is not possible to rate lower than ‘very low certainty’ evidence.

Review authors will generally grade evidence from sound non-randomized studies as low certainty, even if ROBINS-I is used. If, however, such studies yield large effects and there is no obvious bias explaining those effects, review authors may rate the evidence as moderate or – if the effect is large enough – even as high certainty ( Figure 14.2.a ). The very low certainty level is appropriate for, but is not limited to, studies with critical problems and unsystematic clinical observations (e.g. case series or case reports).

Figure 14.2.a Levels of the certainty of a body of evidence in the GRADE approach. *Upgrading criteria are usually applicable to non-randomized studies only (but exceptions exist).

14.2.2 Domains that can lead to decreasing the certainty level of a body of evidence   

We now describe in more detail the five reasons (or domains) for downgrading the certainty of a body of evidence for a specific outcome. In each case, if no reason is found for downgrading the evidence, it should be classified as 'no limitation or not serious' (not important enough to warrant downgrading). If a reason is found for downgrading the evidence, it should be classified as 'serious' (downgrading the certainty rating by one level) or 'very serious' (downgrading the certainty grade by two levels). For non-randomized studies assessed with ROBINS-I, rating down by three levels should be classified as 'extremely' serious.

(1) Risk of bias or limitations in the detailed design and implementation

Our confidence in an estimate of effect decreases if studies suffer from major limitations that are likely to result in a biased assessment of the intervention effect. For randomized trials, these methodological limitations include failure to generate a random sequence, lack of allocation sequence concealment, lack of blinding (particularly with subjective outcomes that are highly susceptible to biased assessment), a large loss to follow-up or selective reporting of outcomes. Chapter 8 provides a discussion of study-level assessments of risk of bias in the context of a Cochrane Review, and proposes an approach to assessing the risk of bias for an outcome across studies as ‘Low’ risk of bias, ‘Some concerns’ and ‘High’ risk of bias for randomized trials. Levels of ‘Low’. ‘Moderate’, ‘Serious’ and ‘Critical’ risk of bias arise for non-randomized studies assessed with ROBINS-I ( Chapter 25 ). These assessments should feed directly into this GRADE domain. In particular, ‘Low’ risk of bias would indicate ‘no limitation’; ‘Some concerns’ would indicate either ‘no limitation’ or ‘serious limitation’; and ‘High’ risk of bias would indicate either ‘serious limitation’ or ‘very serious limitation’. ‘Critical’ risk of bias on ROBINS-I would indicate extremely serious limitations in GRADE. Review authors should use their judgement to decide between alternative categories, depending on the likely magnitude of the potential biases.

Every study addressing a particular outcome will differ, to some degree, in the risk of bias. Review authors should make an overall judgement on whether the certainty of evidence for an outcome warrants downgrading on the basis of study limitations. The assessment of study limitations should apply to the studies contributing to the results in the ‘Summary of findings’ table, rather than to all studies that could potentially be included in the analysis. We have argued in Chapter 7, Section 7.6.2 , that the primary analysis should be restricted to studies at low (or low and unclear) risk of bias where possible.

Table 14.2.a presents the judgements that must be made in going from assessments of the risk of bias to judgements about study limitations for each outcome included in a ‘Summary of findings’ table. A rating of high certainty evidence can be achieved only when most evidence comes from studies that met the criteria for low risk of bias. For example, of the 22 studies addressing the impact of beta-blockers on mortality in patients with heart failure, most probably or certainly used concealed allocation of the sequence, all blinded at least some key groups and follow-up of randomized patients was almost complete (Brophy et al 2001). The certainty of evidence might be downgraded by one level when most of the evidence comes from individual studies either with a crucial limitation for one item, or with some limitations for multiple items. An example of very serious limitations, warranting downgrading by two levels, is provided by evidence on surgery versus conservative treatment in the management of patients with lumbar disc prolapse (Gibson and Waddell 2007). We are uncertain of the benefit of surgery in reducing symptoms after one year or longer, because the one study included in the analysis had inadequate concealment of the allocation sequence and the outcome was assessed using a crude rating by the surgeon without blinding.

(2) Unexplained heterogeneity or inconsistency of results

When studies yield widely differing estimates of effect (heterogeneity or variability in results), investigators should look for robust explanations for that heterogeneity. For instance, drugs may have larger relative effects in sicker populations or when given in larger doses. A detailed discussion of heterogeneity and its investigation is provided in Chapter 10, Section 10.10 and Section 10.11 . If an important modifier exists, with good evidence that important outcomes are different in different subgroups (which would ideally be pre-specified), then a separate ‘Summary of findings’ table may be considered for a separate population. For instance, a separate ‘Summary of findings’ table would be used for carotid endarterectomy in symptomatic patients with high grade stenosis (70% to 99%) in which the intervention is, in the hands of the right surgeons, beneficial, and another (if review authors considered it relevant) for asymptomatic patients with low grade stenosis (less than 30%) in which surgery appears harmful (Orrapin and Rerkasem 2017). When heterogeneity exists and affects the interpretation of results, but review authors are unable to identify a plausible explanation with the data available, the certainty of the evidence decreases.

(3) Indirectness of evidence

Two types of indirectness are relevant. First, a review comparing the effectiveness of alternative interventions (say A and B) may find that randomized trials are available, but they have compared A with placebo and B with placebo. Thus, the evidence is restricted to indirect comparisons between A and B. Where indirect comparisons are undertaken within a network meta-analysis context, GRADE for network meta-analysis should be used (see Chapter 11, Section 11.5 ).

Second, a review may find randomized trials that meet eligibility criteria but address a restricted version of the main review question in terms of population, intervention, comparator or outcomes. For example, suppose that in a review addressing an intervention for secondary prevention of coronary heart disease, most identified studies happened to be in people who also had diabetes. Then the evidence may be regarded as indirect in relation to the broader question of interest because the population is primarily related to people with diabetes. The opposite scenario can equally apply: a review addressing the effect of a preventive strategy for coronary heart disease in people with diabetes may consider studies in people without diabetes to provide relevant, albeit indirect, evidence. This would be particularly likely if investigators had conducted few if any randomized trials in the target population (e.g. people with diabetes). Other sources of indirectness may arise from interventions studied (e.g. if in all included studies a technical intervention was implemented by expert, highly trained specialists in specialist centres, then evidence on the effects of the intervention outside these centres may be indirect), comparators used (e.g. if the comparator groups received an intervention that is less effective than standard treatment in most settings) and outcomes assessed (e.g. indirectness due to surrogate outcomes when data on patient-important outcomes are not available, or when investigators seek data on quality of life but only symptoms are reported). Review authors should make judgements transparent when they believe downgrading is justified, based on differences in anticipated effects in the group of primary interest. Review authors may be aided and increase transparency of their judgements about indirectness if they use Table 14.2.b available in the GRADEpro GDT software (Schünemann et al 2013).

(4) Imprecision of results

When studies include few participants or few events, and thus have wide confidence intervals, review authors can lower their rating of the certainty of the evidence. The confidence intervals included in the ‘Summary of findings’ table will provide readers with information that allows them to make, to some extent, their own rating of precision. Review authors can use a calculation of the optimal information size (OIS) or review information size (RIS), similar to sample size calculations, to make judgements about imprecision (Guyatt et al 2011b, Schünemann 2016). The OIS or RIS is calculated on the basis of the number of participants required for an adequately powered individual study. If the 95% confidence interval excludes a risk ratio (RR) of 1.0, and the total number of events or patients exceeds the OIS criterion, precision is adequate. If the 95% CI includes appreciable benefit or harm (an RR of under 0.75 or over 1.25 is often suggested as a very rough guide) downgrading for imprecision may be appropriate even if OIS criteria are met (Guyatt et al 2011b, Schünemann 2016).

(5) High probability of publication bias

The certainty of evidence level may be downgraded if investigators fail to report studies on the basis of results (typically those that show no effect: publication bias) or outcomes (typically those that may be harmful or for which no effect was observed: selective outcome non-reporting bias). Selective reporting of outcomes from among multiple outcomes measured is assessed at the study level as part of the assessment of risk of bias (see Chapter 8, Section 8.7 ), so for the studies contributing to the outcome in the ‘Summary of findings’ table this is addressed by domain 1 above (limitations in the design and implementation). If a large number of studies included in the review do not contribute to an outcome, or if there is evidence of publication bias, the certainty of the evidence may be downgraded. Chapter 13 provides a detailed discussion of reporting biases, including publication bias, and how it may be tackled in a Cochrane Review. A prototypical situation that may elicit suspicion of publication bias is when published evidence includes a number of small studies, all of which are industry-funded (Bhandari et al 2004). For example, 14 studies of flavanoids in patients with haemorrhoids have shown apparent large benefits, but enrolled a total of only 1432 patients (i.e. each study enrolled relatively few patients) (Alonso-Coello et al 2006). The heavy involvement of sponsors in most of these studies raises questions of whether unpublished studies that suggest no benefit exist (publication bias).

A particular body of evidence can suffer from problems associated with more than one of the five factors listed here, and the greater the problems, the lower the certainty of evidence rating that should result. One could imagine a situation in which randomized trials were available, but all or virtually all of these limitations would be present, and in serious form. A very low certainty of evidence rating would result.

Table 14.2.a Further guidelines for domain 1 (of 5) in a GRADE assessment: going from assessments of risk of bias in studies to judgements about study limitations for main outcomes across studies

Table 14.2.b Judgements about indirectness by outcome (available in GRADEpro GDT)

Intervention:

Comparator:

Direct comparison:

Final judgement about indirectness across domains:

14.2.3 Domains that may lead to increasing the certainty level of a body of evidence

Although NRSI and downgraded randomized trials will generally yield a low rating for certainty of evidence, there will be unusual circumstances in which review authors could ‘upgrade’ such evidence to moderate or even high certainty ( Table 14.3.a ).

• Large effects On rare occasions when methodologically well-done observational studies yield large, consistent and precise estimates of the magnitude of an intervention effect, one may be particularly confident in the results. A large estimated effect (e.g. RR >2 or RR <0.5) in the absence of plausible confounders, or a very large effect (e.g. RR >5 or RR <0.2) in studies with no major threats to validity, might qualify for this. In these situations, while the NRSI may possibly have provided an over-estimate of the true effect, the weak study design may not explain all of the apparent observed benefit. Thus, despite reservations based on the observational study design, review authors are confident that the effect exists. The magnitude of the effect in these studies may move the assigned certainty of evidence from low to moderate (if the effect is large in the absence of other methodological limitations). For example, a meta-analysis of observational studies showed that bicycle helmets reduce the risk of head injuries in cyclists by a large margin (odds ratio (OR) 0.31, 95% CI 0.26 to 0.37) (Thompson et al 2000). This large effect, in the absence of obvious bias that could create the association, suggests a rating of moderate-certainty evidence.  Note : GRADE guidance suggests the possibility of rating up one level for a large effect if the relative effect is greater than 2.0. However, if the point estimate of the relative effect is greater than 2.0, but the confidence interval is appreciably below 2.0, then some hesitation would be appropriate in the decision to rate up for a large effect. Another situation allows inference of a strong association without a formal comparative study. Consider the question of the impact of routine colonoscopy versus no screening for colon cancer on the rate of perforation associated with colonoscopy. Here, a large series of representative patients undergoing colonoscopy may provide high certainty evidence about the risk of perforation associated with colonoscopy. When the risk of the event among patients receiving the relevant comparator is known to be near 0 (i.e. we are certain that the incidence of spontaneous colon perforation in patients not undergoing colonoscopy is extremely low), case series or cohort studies of representative patients can provide high certainty evidence of adverse effects associated with an intervention, thereby allowing us to infer a strong association from even a limited number of events.
• Dose-response The presence of a dose-response gradient may increase our confidence in the findings of observational studies and thereby enhance the assigned certainty of evidence. For example, our confidence in the result of observational studies that show an increased risk of bleeding in patients who have supratherapeutic anticoagulation levels is increased by the observation that there is a dose-response gradient between the length of time needed for blood to clot (as measured by the international normalized ratio (INR)) and an increased risk of bleeding (Levine et al 2004). A systematic review of NRSI investigating the effect of cyclooxygenase-2 inhibitors on cardiovascular events found that the summary estimate (RR) with rofecoxib was 1.33 (95% CI 1.00 to 1.79) with doses less than 25mg/d, and 2.19 (95% CI 1.64 to 2.91) with doses more than 25mg/d. Although residual confounding is likely to exist in the NRSI that address this issue, the existence of a dose-response gradient and the large apparent effect of higher doses of rofecoxib markedly increase our strength of inference that the association cannot be explained by residual confounding, and is therefore likely to be both causal and, at high levels of exposure, substantial.  Note : GRADE guidance suggests the possibility of rating up one level for a large effect if the relative effect is greater than 2.0. Here, the fact that the point estimate of the relative effect is greater than 2.0, but the confidence interval is appreciably below 2.0 might make some hesitate in the decision to rate up for a large effect
• Plausible confounding On occasion, all plausible biases from randomized or non-randomized studies may be working to under-estimate an apparent intervention effect. For example, if only sicker patients receive an experimental intervention or exposure, yet they still fare better, it is likely that the actual intervention or exposure effect is larger than the data suggest. For instance, a rigorous systematic review of observational studies including a total of 38 million patients demonstrated higher death rates in private for-profit versus private not-for-profit hospitals (Devereaux et al 2002). One possible bias relates to different disease severity in patients in the two hospital types. It is likely, however, that patients in the not-for-profit hospitals were sicker than those in the for-profit hospitals. Thus, to the extent that residual confounding existed, it would bias results against the not-for-profit hospitals. The second likely bias was the possibility that higher numbers of patients with excellent private insurance coverage could lead to a hospital having more resources and a spill-over effect that would benefit those without such coverage. Since for-profit hospitals are likely to admit a larger proportion of such well-insured patients than not-for-profit hospitals, the bias is once again against the not-for-profit hospitals. Since the plausible biases would all diminish the demonstrated intervention effect, one might consider the evidence from these observational studies as moderate rather than low certainty. A parallel situation exists when observational studies have failed to demonstrate an association, but all plausible biases would have increased an intervention effect. This situation will usually arise in the exploration of apparent harmful effects. For example, because the hypoglycaemic drug phenformin causes lactic acidosis, the related agent metformin was under suspicion for the same toxicity. Nevertheless, very large observational studies have failed to demonstrate an association (Salpeter et al 2007). Given the likelihood that clinicians would be more alert to lactic acidosis in the presence of the agent and over-report its occurrence, one might consider this moderate, or even high certainty, evidence refuting a causal relationship between typical therapeutic doses of metformin and lactic acidosis.

14.3 Describing the assessment of the certainty of a body of evidence using the GRADE framework

Review authors should report the grading of the certainty of evidence in the Results section for each outcome for which this has been performed, providing the rationale for downgrading or upgrading the evidence, and referring to the ‘Summary of findings’ table where applicable.

Table 14.3.a provides a framework and examples for how review authors can justify their judgements about the certainty of evidence in each domain. These justifications should also be included in explanatory notes to the ‘Summary of Findings’ table (see Section 14.1.6.10 ).

Chapter 15, Section 15.6 , describes in more detail how the overall GRADE assessment across all domains can be used to draw conclusions about the effects of the intervention, as well as providing implications for future research.

Table 14.3.a Framework for describing the certainty of evidence and justifying downgrading or upgrading

14.4 Chapter information

Authors: Holger J Schünemann, Julian PT Higgins, Gunn E Vist, Paul Glasziou, Elie A Akl, Nicole Skoetz, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group (formerly Applicability and Recommendations Methods Group) and the Cochrane Statistical Methods Group

Acknowledgements: Andrew D Oxman contributed to earlier versions. Professor Penny Hawe contributed to the text on adverse effects in earlier versions. Jon Deeks provided helpful contributions on an earlier version of this chapter. For details of previous authors and editors of the Handbook , please refer to the Preface.

Funding: This work was in part supported by funding from the Michael G DeGroote Cochrane Canada Centre and the Ontario Ministry of Health.

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Schünemann HJ, Best D, Vist G, Oxman AD, Group GW. Letters, numbers, symbols and words: how to communicate grades of evidence and recommendations. Canadian Medical Association Journal 2003; 169 : 677-680.

Schünemann HJ, Jaeschke R, Cook DJ, Bria WF, El-Solh AA, Ernst A, Fahy BF, Gould MK, Horan KL, Krishnan JA, Manthous CA, Maurer JR, McNicholas WT, Oxman AD, Rubenfeld G, Turino GM, Guyatt G. An official ATS statement: grading the quality of evidence and strength of recommendations in ATS guidelines and recommendations. American Journal of Respiratory and Critical Care Medicine 2006; 174 : 605-614.

Schünemann HJ, Oxman AD, Brozek J, Glasziou P, Jaeschke R, Vist GE, Williams JW, Jr., Kunz R, Craig J, Montori VM, Bossuyt P, Guyatt GH. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies. BMJ 2008a; 336 : 1106-1110.

Schünemann HJ, Oxman AD, Brozek J, Glasziou P, Bossuyt P, Chang S, Muti P, Jaeschke R, Guyatt GH. GRADE: assessing the quality of evidence for diagnostic recommendations. ACP Journal Club 2008b; 149 : 2.

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Schünemann HJ, Tugwell P, Reeves BC, Akl EA, Santesso N, Spencer FA, Shea B, Wells G, Helfand M. Non-randomized studies as a source of complementary, sequential or replacement evidence for randomized controlled trials in systematic reviews on the effects of interventions. Research Synthesis Methods 2013; 4 : 49-62.

Schünemann HJ. Interpreting GRADE's levels of certainty or quality of the evidence: GRADE for statisticians, considering review information size or less emphasis on imprecision? Journal of Clinical Epidemiology 2016; 75 : 6-15.

Schünemann HJ, Cuello C, Akl EA, Mustafa RA, Meerpohl JJ, Thayer K, Morgan RL, Gartlehner G, Kunz R, Katikireddi SV, Sterne J, Higgins JPT, Guyatt G, Group GW. GRADE guidelines: 18. How ROBINS-I and other tools to assess risk of bias in nonrandomized studies should be used to rate the certainty of a body of evidence. Journal of Clinical Epidemiology 2018.

Spencer-Bonilla G, Quinones AR, Montori VM, International Minimally Disruptive Medicine W. Assessing the Burden of Treatment. Journal of General Internal Medicine 2017; 32 : 1141-1145.

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The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews

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• Peer review
• Joanne E McKenzie , associate professor 1 ,
• Patrick M Bossuyt , professor 2 ,
• Isabelle Boutron , professor 3 ,
• Tammy C Hoffmann , professor 4 ,
• Cynthia D Mulrow , professor 5 ,
• Larissa Shamseer , doctoral student 6 ,
• Jennifer M Tetzlaff , research product specialist 7 ,
• Elie A Akl , professor 8 ,
• Sue E Brennan , senior research fellow 1 ,
• Roger Chou , professor 9 ,
• Julie Glanville , associate director 10 ,
• Jeremy M Grimshaw , professor 11 ,
• Asbjørn Hróbjartsson , professor 12 ,
• Manoj M Lalu , associate scientist and assistant professor 13 ,
• Tianjing Li , associate professor 14 ,
• Elizabeth W Loder , professor 15 ,
• Evan Mayo-Wilson , associate professor 16 ,
• Steve McDonald , senior research fellow 1 ,
• Luke A McGuinness , research associate 17 ,
• Lesley A Stewart , professor and director 18 ,
• James Thomas , professor 19 ,
• Andrea C Tricco , scientist and associate professor 20 ,
• Vivian A Welch , associate professor 21 ,
• Penny Whiting , associate professor 17 ,
• David Moher , director and professor 22
• 1 School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
• 2 Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, Netherlands
• 3 Université de Paris, Centre of Epidemiology and Statistics (CRESS), Inserm, F 75004 Paris, France
• 4 Institute for Evidence-Based Healthcare, Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Australia
• 5 University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA; Annals of Internal Medicine
• 6 Knowledge Translation Program, Li Ka Shing Knowledge Institute, Toronto, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada
• 7 Evidence Partners, Ottawa, Canada
• 8 Clinical Research Institute, American University of Beirut, Beirut, Lebanon; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
• 9 Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
• 10 York Health Economics Consortium (YHEC Ltd), University of York, York, UK
• 11 Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada; Department of Medicine, University of Ottawa, Ottawa, Canada
• 12 Centre for Evidence-Based Medicine Odense (CEBMO) and Cochrane Denmark, Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Open Patient data Exploratory Network (OPEN), Odense University Hospital, Odense, Denmark
• 13 Department of Anesthesiology and Pain Medicine, The Ottawa Hospital, Ottawa, Canada; Clinical Epidemiology Program, Blueprint Translational Research Group, Ottawa Hospital Research Institute, Ottawa, Canada; Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, Canada
• 14 Department of Ophthalmology, School of Medicine, University of Colorado Denver, Denver, Colorado, United States; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
• 15 Division of Headache, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Head of Research, The BMJ , London, UK
• 16 Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
• 17 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
• 18 Centre for Reviews and Dissemination, University of York, York, UK
• 19 EPPI-Centre, UCL Social Research Institute, University College London, London, UK
• 20 Li Ka Shing Knowledge Institute of St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; Epidemiology Division of the Dalla Lana School of Public Health and the Institute of Health Management, Policy, and Evaluation, University of Toronto, Toronto, Canada; Queen's Collaboration for Health Care Quality Joanna Briggs Institute Centre of Excellence, Queen's University, Kingston, Canada
• 21 Methods Centre, Bruyère Research Institute, Ottawa, Ontario, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada
• 22 Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada
• Correspondence to: M J Page matthew.page{at}monash.edu
• Accepted 4 January 2021

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

Systematic reviews serve many critical roles. They can provide syntheses of the state of knowledge in a field, from which future research priorities can be identified; they can address questions that otherwise could not be answered by individual studies; they can identify problems in primary research that should be rectified in future studies; and they can generate or evaluate theories about how or why phenomena occur. Systematic reviews therefore generate various types of knowledge for different users of reviews (such as patients, healthcare providers, researchers, and policy makers). 1 2 To ensure a systematic review is valuable to users, authors should prepare a transparent, complete, and accurate account of why the review was done, what they did (such as how studies were identified and selected) and what they found (such as characteristics of contributing studies and results of meta-analyses). Up-to-date reporting guidance facilitates authors achieving this. 3

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement published in 2009 (hereafter referred to as PRISMA 2009) 4 5 6 7 8 9 10 is a reporting guideline designed to address poor reporting of systematic reviews. 11 The PRISMA 2009 statement comprised a checklist of 27 items recommended for reporting in systematic reviews and an “explanation and elaboration” paper 12 13 14 15 16 providing additional reporting guidance for each item, along with exemplars of reporting. The recommendations have been widely endorsed and adopted, as evidenced by its co-publication in multiple journals, citation in over 60 000 reports (Scopus, August 2020), endorsement from almost 200 journals and systematic review organisations, and adoption in various disciplines. Evidence from observational studies suggests that use of the PRISMA 2009 statement is associated with more complete reporting of systematic reviews, 17 18 19 20 although more could be done to improve adherence to the guideline. 21

Many innovations in the conduct of systematic reviews have occurred since publication of the PRISMA 2009 statement. For example, technological advances have enabled the use of natural language processing and machine learning to identify relevant evidence, 22 23 24 methods have been proposed to synthesise and present findings when meta-analysis is not possible or appropriate, 25 26 27 and new methods have been developed to assess the risk of bias in results of included studies. 28 29 Evidence on sources of bias in systematic reviews has accrued, culminating in the development of new tools to appraise the conduct of systematic reviews. 30 31 Terminology used to describe particular review processes has also evolved, as in the shift from assessing “quality” to assessing “certainty” in the body of evidence. 32 In addition, the publishing landscape has transformed, with multiple avenues now available for registering and disseminating systematic review protocols, 33 34 disseminating reports of systematic reviews, and sharing data and materials, such as preprint servers and publicly accessible repositories. To capture these advances in the reporting of systematic reviews necessitated an update to the PRISMA 2009 statement.

Summary points

To ensure a systematic review is valuable to users, authors should prepare a transparent, complete, and accurate account of why the review was done, what they did, and what they found

The PRISMA 2020 statement provides updated reporting guidance for systematic reviews that reflects advances in methods to identify, select, appraise, and synthesise studies

The PRISMA 2020 statement consists of a 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and revised flow diagrams for original and updated reviews

We anticipate that the PRISMA 2020 statement will benefit authors, editors, and peer reviewers of systematic reviews, and different users of reviews, including guideline developers, policy makers, healthcare providers, patients, and other stakeholders

Development of PRISMA 2020

A complete description of the methods used to develop PRISMA 2020 is available elsewhere. 35 We identified PRISMA 2009 items that were often reported incompletely by examining the results of studies investigating the transparency of reporting of published reviews. 17 21 36 37 We identified possible modifications to the PRISMA 2009 statement by reviewing 60 documents providing reporting guidance for systematic reviews (including reporting guidelines, handbooks, tools, and meta-research studies). 38 These reviews of the literature were used to inform the content of a survey with suggested possible modifications to the 27 items in PRISMA 2009 and possible additional items. Respondents were asked whether they believed we should keep each PRISMA 2009 item as is, modify it, or remove it, and whether we should add each additional item. Systematic review methodologists and journal editors were invited to complete the online survey (110 of 220 invited responded). We discussed proposed content and wording of the PRISMA 2020 statement, as informed by the review and survey results, at a 21-member, two-day, in-person meeting in September 2018 in Edinburgh, Scotland. Throughout 2019 and 2020, we circulated an initial draft and five revisions of the checklist and explanation and elaboration paper to co-authors for feedback. In April 2020, we invited 22 systematic reviewers who had expressed interest in providing feedback on the PRISMA 2020 checklist to share their views (via an online survey) on the layout and terminology used in a preliminary version of the checklist. Feedback was received from 15 individuals and considered by the first author, and any revisions deemed necessary were incorporated before the final version was approved and endorsed by all co-authors.

The PRISMA 2020 statement

Scope of the guideline.

The PRISMA 2020 statement has been designed primarily for systematic reviews of studies that evaluate the effects of health interventions, irrespective of the design of the included studies. However, the checklist items are applicable to reports of systematic reviews evaluating other interventions (such as social or educational interventions), and many items are applicable to systematic reviews with objectives other than evaluating interventions (such as evaluating aetiology, prevalence, or prognosis). PRISMA 2020 is intended for use in systematic reviews that include synthesis (such as pairwise meta-analysis or other statistical synthesis methods) or do not include synthesis (for example, because only one eligible study is identified). The PRISMA 2020 items are relevant for mixed-methods systematic reviews (which include quantitative and qualitative studies), but reporting guidelines addressing the presentation and synthesis of qualitative data should also be consulted. 39 40 PRISMA 2020 can be used for original systematic reviews, updated systematic reviews, or continually updated (“living”) systematic reviews. However, for updated and living systematic reviews, there may be some additional considerations that need to be addressed. Where there is relevant content from other reporting guidelines, we reference these guidelines within the items in the explanation and elaboration paper 41 (such as PRISMA-Search 42 in items 6 and 7, Synthesis without meta-analysis (SWiM) reporting guideline 27 in item 13d). Box 1 includes a glossary of terms used throughout the PRISMA 2020 statement.

Glossary of terms

Systematic review —A review that uses explicit, systematic methods to collate and synthesise findings of studies that address a clearly formulated question 43

Statistical synthesis —The combination of quantitative results of two or more studies. This encompasses meta-analysis of effect estimates (described below) and other methods, such as combining P values, calculating the range and distribution of observed effects, and vote counting based on the direction of effect (see McKenzie and Brennan 25 for a description of each method)

Meta-analysis of effect estimates —A statistical technique used to synthesise results when study effect estimates and their variances are available, yielding a quantitative summary of results 25

Outcome —An event or measurement collected for participants in a study (such as quality of life, mortality)

Result —The combination of a point estimate (such as a mean difference, risk ratio, or proportion) and a measure of its precision (such as a confidence/credible interval) for a particular outcome

Report —A document (paper or electronic) supplying information about a particular study. It could be a journal article, preprint, conference abstract, study register entry, clinical study report, dissertation, unpublished manuscript, government report, or any other document providing relevant information

Record —The title or abstract (or both) of a report indexed in a database or website (such as a title or abstract for an article indexed in Medline). Records that refer to the same report (such as the same journal article) are “duplicates”; however, records that refer to reports that are merely similar (such as a similar abstract submitted to two different conferences) should be considered unique.

Study —An investigation, such as a clinical trial, that includes a defined group of participants and one or more interventions and outcomes. A “study” might have multiple reports. For example, reports could include the protocol, statistical analysis plan, baseline characteristics, results for the primary outcome, results for harms, results for secondary outcomes, and results for additional mediator and moderator analyses

PRISMA 2020 is not intended to guide systematic review conduct, for which comprehensive resources are available. 43 44 45 46 However, familiarity with PRISMA 2020 is useful when planning and conducting systematic reviews to ensure that all recommended information is captured. PRISMA 2020 should not be used to assess the conduct or methodological quality of systematic reviews; other tools exist for this purpose. 30 31 Furthermore, PRISMA 2020 is not intended to inform the reporting of systematic review protocols, for which a separate statement is available (PRISMA for Protocols (PRISMA-P) 2015 statement 47 48 ). Finally, extensions to the PRISMA 2009 statement have been developed to guide reporting of network meta-analyses, 49 meta-analyses of individual participant data, 50 systematic reviews of harms, 51 systematic reviews of diagnostic test accuracy studies, 52 and scoping reviews 53 ; for these types of reviews we recommend authors report their review in accordance with the recommendations in PRISMA 2020 along with the guidance specific to the extension.

How to use PRISMA 2020

The PRISMA 2020 statement (including the checklists, explanation and elaboration, and flow diagram) replaces the PRISMA 2009 statement, which should no longer be used. Box 2 summarises noteworthy changes from the PRISMA 2009 statement. The PRISMA 2020 checklist includes seven sections with 27 items, some of which include sub-items ( table 1 ). A checklist for journal and conference abstracts for systematic reviews is included in PRISMA 2020. This abstract checklist is an update of the 2013 PRISMA for Abstracts statement, 54 reflecting new and modified content in PRISMA 2020 ( table 2 ). A template PRISMA flow diagram is provided, which can be modified depending on whether the systematic review is original or updated ( fig 1 ).

Noteworthy changes to the PRISMA 2009 statement

Inclusion of the abstract reporting checklist within PRISMA 2020 (see item #2 and table 2 ).

Movement of the ‘Protocol and registration’ item from the start of the Methods section of the checklist to a new Other section, with addition of a sub-item recommending authors describe amendments to information provided at registration or in the protocol (see item #24a-24c).

Modification of the ‘Search’ item to recommend authors present full search strategies for all databases, registers and websites searched, not just at least one database (see item #7).

Modification of the ‘Study selection’ item in the Methods section to emphasise the reporting of how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process (see item #8).

Addition of a sub-item to the ‘Data items’ item recommending authors report how outcomes were defined, which results were sought, and methods for selecting a subset of results from included studies (see item #10a).

Splitting of the ‘Synthesis of results’ item in the Methods section into six sub-items recommending authors describe: the processes used to decide which studies were eligible for each synthesis; any methods required to prepare the data for synthesis; any methods used to tabulate or visually display results of individual studies and syntheses; any methods used to synthesise results; any methods used to explore possible causes of heterogeneity among study results (such as subgroup analysis, meta-regression); and any sensitivity analyses used to assess robustness of the synthesised results (see item #13a-13f).

Addition of a sub-item to the ‘Study selection’ item in the Results section recommending authors cite studies that might appear to meet the inclusion criteria, but which were excluded, and explain why they were excluded (see item #16b).

Splitting of the ‘Synthesis of results’ item in the Results section into four sub-items recommending authors: briefly summarise the characteristics and risk of bias among studies contributing to the synthesis; present results of all statistical syntheses conducted; present results of any investigations of possible causes of heterogeneity among study results; and present results of any sensitivity analyses (see item #20a-20d).

Addition of new items recommending authors report methods for and results of an assessment of certainty (or confidence) in the body of evidence for an outcome (see items #15 and #22).

Addition of a new item recommending authors declare any competing interests (see item #26).

Addition of a new item recommending authors indicate whether data, analytic code and other materials used in the review are publicly available and if so, where they can be found (see item #27).

PRISMA 2020 item checklist

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PRISMA 2020 for Abstracts checklist*

PRISMA 2020 flow diagram template for systematic reviews. The new design is adapted from flow diagrams proposed by Boers, 55 Mayo-Wilson et al. 56 and Stovold et al. 57 The boxes in grey should only be completed if applicable; otherwise they should be removed from the flow diagram. Note that a “report” could be a journal article, preprint, conference abstract, study register entry, clinical study report, dissertation, unpublished manuscript, government report or any other document providing relevant information.

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We recommend authors refer to PRISMA 2020 early in the writing process, because prospective consideration of the items may help to ensure that all the items are addressed. To help keep track of which items have been reported, the PRISMA statement website ( http://www.prisma-statement.org/ ) includes fillable templates of the checklists to download and complete (also available in the data supplement on bmj.com). We have also created a web application that allows users to complete the checklist via a user-friendly interface 58 (available at https://prisma.shinyapps.io/checklist/ and adapted from the Transparency Checklist app 59 ). The completed checklist can be exported to Word or PDF. Editable templates of the flow diagram can also be downloaded from the PRISMA statement website.

We have prepared an updated explanation and elaboration paper, in which we explain why reporting of each item is recommended and present bullet points that detail the reporting recommendations (which we refer to as elements). 41 The bullet-point structure is new to PRISMA 2020 and has been adopted to facilitate implementation of the guidance. 60 61 An expanded checklist, which comprises an abridged version of the elements presented in the explanation and elaboration paper, with references and some examples removed, is available in the data supplement on bmj.com. Consulting the explanation and elaboration paper is recommended if further clarity or information is required.

Journals and publishers might impose word and section limits, and limits on the number of tables and figures allowed in the main report. In such cases, if the relevant information for some items already appears in a publicly accessible review protocol, referring to the protocol may suffice. Alternatively, placing detailed descriptions of the methods used or additional results (such as for less critical outcomes) in supplementary files is recommended. Ideally, supplementary files should be deposited to a general-purpose or institutional open-access repository that provides free and permanent access to the material (such as Open Science Framework, Dryad, figshare). A reference or link to the additional information should be included in the main report. Finally, although PRISMA 2020 provides a template for where information might be located, the suggested location should not be seen as prescriptive; the guiding principle is to ensure the information is reported.

Use of PRISMA 2020 has the potential to benefit many stakeholders. Complete reporting allows readers to assess the appropriateness of the methods, and therefore the trustworthiness of the findings. Presenting and summarising characteristics of studies contributing to a synthesis allows healthcare providers and policy makers to evaluate the applicability of the findings to their setting. Describing the certainty in the body of evidence for an outcome and the implications of findings should help policy makers, managers, and other decision makers formulate appropriate recommendations for practice or policy. Complete reporting of all PRISMA 2020 items also facilitates replication and review updates, as well as inclusion of systematic reviews in overviews (of systematic reviews) and guidelines, so teams can leverage work that is already done and decrease research waste. 36 62 63

We updated the PRISMA 2009 statement by adapting the EQUATOR Network’s guidance for developing health research reporting guidelines. 64 We evaluated the reporting completeness of published systematic reviews, 17 21 36 37 reviewed the items included in other documents providing guidance for systematic reviews, 38 surveyed systematic review methodologists and journal editors for their views on how to revise the original PRISMA statement, 35 discussed the findings at an in-person meeting, and prepared this document through an iterative process. Our recommendations are informed by the reviews and survey conducted before the in-person meeting, theoretical considerations about which items facilitate replication and help users assess the risk of bias and applicability of systematic reviews, and co-authors’ experience with authoring and using systematic reviews.

Various strategies to increase the use of reporting guidelines and improve reporting have been proposed. They include educators introducing reporting guidelines into graduate curricula to promote good reporting habits of early career scientists 65 ; journal editors and regulators endorsing use of reporting guidelines 18 ; peer reviewers evaluating adherence to reporting guidelines 61 66 ; journals requiring authors to indicate where in their manuscript they have adhered to each reporting item 67 ; and authors using online writing tools that prompt complete reporting at the writing stage. 60 Multi-pronged interventions, where more than one of these strategies are combined, may be more effective (such as completion of checklists coupled with editorial checks). 68 However, of 31 interventions proposed to increase adherence to reporting guidelines, the effects of only 11 have been evaluated, mostly in observational studies at high risk of bias due to confounding. 69 It is therefore unclear which strategies should be used. Future research might explore barriers and facilitators to the use of PRISMA 2020 by authors, editors, and peer reviewers, designing interventions that address the identified barriers, and evaluating those interventions using randomised trials. To inform possible revisions to the guideline, it would also be valuable to conduct think-aloud studies 70 to understand how systematic reviewers interpret the items, and reliability studies to identify items where there is varied interpretation of the items.

We encourage readers to submit evidence that informs any of the recommendations in PRISMA 2020 (via the PRISMA statement website: http://www.prisma-statement.org/ ). To enhance accessibility of PRISMA 2020, several translations of the guideline are under way (see available translations at the PRISMA statement website). We encourage journal editors and publishers to raise awareness of PRISMA 2020 (for example, by referring to it in journal “Instructions to authors”), endorsing its use, advising editors and peer reviewers to evaluate submitted systematic reviews against the PRISMA 2020 checklists, and making changes to journal policies to accommodate the new reporting recommendations. We recommend existing PRISMA extensions 47 49 50 51 52 53 71 72 be updated to reflect PRISMA 2020 and advise developers of new PRISMA extensions to use PRISMA 2020 as the foundation document.

We anticipate that the PRISMA 2020 statement will benefit authors, editors, and peer reviewers of systematic reviews, and different users of reviews, including guideline developers, policy makers, healthcare providers, patients, and other stakeholders. Ultimately, we hope that uptake of the guideline will lead to more transparent, complete, and accurate reporting of systematic reviews, thus facilitating evidence based decision making.

Acknowledgments

We dedicate this paper to the late Douglas G Altman and Alessandro Liberati, whose contributions were fundamental to the development and implementation of the original PRISMA statement.

We thank the following contributors who completed the survey to inform discussions at the development meeting: Xavier Armoiry, Edoardo Aromataris, Ana Patricia Ayala, Ethan M Balk, Virginia Barbour, Elaine Beller, Jesse A Berlin, Lisa Bero, Zhao-Xiang Bian, Jean Joel Bigna, Ferrán Catalá-López, Anna Chaimani, Mike Clarke, Tammy Clifford, Ioana A Cristea, Miranda Cumpston, Sofia Dias, Corinna Dressler, Ivan D Florez, Joel J Gagnier, Chantelle Garritty, Long Ge, Davina Ghersi, Sean Grant, Gordon Guyatt, Neal R Haddaway, Julian PT Higgins, Sally Hopewell, Brian Hutton, Jamie J Kirkham, Jos Kleijnen, Julia Koricheva, Joey SW Kwong, Toby J Lasserson, Julia H Littell, Yoon K Loke, Malcolm R Macleod, Chris G Maher, Ana Marušic, Dimitris Mavridis, Jessie McGowan, Matthew DF McInnes, Philippa Middleton, Karel G Moons, Zachary Munn, Jane Noyes, Barbara Nußbaumer-Streit, Donald L Patrick, Tatiana Pereira-Cenci, Ba’ Pham, Bob Phillips, Dawid Pieper, Michelle Pollock, Daniel S Quintana, Drummond Rennie, Melissa L Rethlefsen, Hannah R Rothstein, Maroeska M Rovers, Rebecca Ryan, Georgia Salanti, Ian J Saldanha, Margaret Sampson, Nancy Santesso, Rafael Sarkis-Onofre, Jelena Savović, Christopher H Schmid, Kenneth F Schulz, Guido Schwarzer, Beverley J Shea, Paul G Shekelle, Farhad Shokraneh, Mark Simmonds, Nicole Skoetz, Sharon E Straus, Anneliese Synnot, Emily E Tanner-Smith, Brett D Thombs, Hilary Thomson, Alexander Tsertsvadze, Peter Tugwell, Tari Turner, Lesley Uttley, Jeffrey C Valentine, Matt Vassar, Areti Angeliki Veroniki, Meera Viswanathan, Cole Wayant, Paul Whaley, and Kehu Yang. We thank the following contributors who provided feedback on a preliminary version of the PRISMA 2020 checklist: Jo Abbott, Fionn Büttner, Patricia Correia-Santos, Victoria Freeman, Emily A Hennessy, Rakibul Islam, Amalia (Emily) Karahalios, Kasper Krommes, Andreas Lundh, Dafne Port Nascimento, Davina Robson, Catherine Schenck-Yglesias, Mary M Scott, Sarah Tanveer and Pavel Zhelnov. We thank Abigail H Goben, Melissa L Rethlefsen, Tanja Rombey, Anna Scott, and Farhad Shokraneh for their helpful comments on the preprints of the PRISMA 2020 papers. We thank Edoardo Aromataris, Stephanie Chang, Toby Lasserson and David Schriger for their helpful peer review comments on the PRISMA 2020 papers.

Contributors: JEM and DM are joint senior authors. MJP, JEM, PMB, IB, TCH, CDM, LS, and DM conceived this paper and designed the literature review and survey conducted to inform the guideline content. MJP conducted the literature review, administered the survey and analysed the data for both. MJP prepared all materials for the development meeting. MJP and JEM presented proposals at the development meeting. All authors except for TCH, JMT, EAA, SEB, and LAM attended the development meeting. MJP and JEM took and consolidated notes from the development meeting. MJP and JEM led the drafting and editing of the article. JEM, PMB, IB, TCH, LS, JMT, EAA, SEB, RC, JG, AH, TL, EMW, SM, LAM, LAS, JT, ACT, PW, and DM drafted particular sections of the article. All authors were involved in revising the article critically for important intellectual content. All authors approved the final version of the article. MJP is the guarantor of this work. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: There was no direct funding for this research. MJP is supported by an Australian Research Council Discovery Early Career Researcher Award (DE200101618) and was previously supported by an Australian National Health and Medical Research Council (NHMRC) Early Career Fellowship (1088535) during the conduct of this research. JEM is supported by an Australian NHMRC Career Development Fellowship (1143429). TCH is supported by an Australian NHMRC Senior Research Fellowship (1154607). JMT is supported by Evidence Partners Inc. JMG is supported by a Tier 1 Canada Research Chair in Health Knowledge Transfer and Uptake. MML is supported by The Ottawa Hospital Anaesthesia Alternate Funds Association and a Faculty of Medicine Junior Research Chair. TL is supported by funding from the National Eye Institute (UG1EY020522), National Institutes of Health, United States. LAM is supported by a National Institute for Health Research Doctoral Research Fellowship (DRF-2018-11-ST2-048). ACT is supported by a Tier 2 Canada Research Chair in Knowledge Synthesis. DM is supported in part by a University Research Chair, University of Ottawa. The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at http://www.icmje.org/conflicts-of-interest/ and declare: EL is head of research for the BMJ ; MJP is an editorial board member for PLOS Medicine ; ACT is an associate editor and MJP, TL, EMW, and DM are editorial board members for the Journal of Clinical Epidemiology ; DM and LAS were editors in chief, LS, JMT, and ACT are associate editors, and JG is an editorial board member for Systematic Reviews . None of these authors were involved in the peer review process or decision to publish. TCH has received personal fees from Elsevier outside the submitted work. EMW has received personal fees from the American Journal for Public Health , for which he is the editor for systematic reviews. VW is editor in chief of the Campbell Collaboration, which produces systematic reviews, and co-convenor of the Campbell and Cochrane equity methods group. DM is chair of the EQUATOR Network, IB is adjunct director of the French EQUATOR Centre and TCH is co-director of the Australasian EQUATOR Centre, which advocates for the use of reporting guidelines to improve the quality of reporting in research articles. JMT received salary from Evidence Partners, creator of DistillerSR software for systematic reviews; Evidence Partners was not involved in the design or outcomes of the statement, and the views expressed solely represent those of the author.

Provenance and peer review: Not commissioned; externally peer reviewed.

Patient and public involvement: Patients and the public were not involved in this methodological research. We plan to disseminate the research widely, including to community participants in evidence synthesis organisations.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

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Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.

Handbook of eHealth Evaluation: An Evidence-based Approach [Internet].

Chapter 9 methods for literature reviews.

Guy Paré and Spyros Kitsiou .

9.1. Introduction

Literature reviews play a critical role in scholarship because science remains, first and foremost, a cumulative endeavour ( vom Brocke et al., 2009 ). As in any academic discipline, rigorous knowledge syntheses are becoming indispensable in keeping up with an exponentially growing eHealth literature, assisting practitioners, academics, and graduate students in finding, evaluating, and synthesizing the contents of many empirical and conceptual papers. Among other methods, literature reviews are essential for: (a) identifying what has been written on a subject or topic; (b) determining the extent to which a specific research area reveals any interpretable trends or patterns; (c) aggregating empirical findings related to a narrow research question to support evidence-based practice; (d) generating new frameworks and theories; and (e) identifying topics or questions requiring more investigation ( Paré, Trudel, Jaana, & Kitsiou, 2015 ).

Literature reviews can take two major forms. The most prevalent one is the “literature review” or “background” section within a journal paper or a chapter in a graduate thesis. This section synthesizes the extant literature and usually identifies the gaps in knowledge that the empirical study addresses ( Sylvester, Tate, & Johnstone, 2013 ). It may also provide a theoretical foundation for the proposed study, substantiate the presence of the research problem, justify the research as one that contributes something new to the cumulated knowledge, or validate the methods and approaches for the proposed study ( Hart, 1998 ; Levy & Ellis, 2006 ).

The second form of literature review, which is the focus of this chapter, constitutes an original and valuable work of research in and of itself ( Paré et al., 2015 ). Rather than providing a base for a researcher’s own work, it creates a solid starting point for all members of the community interested in a particular area or topic ( Mulrow, 1987 ). The so-called “review article” is a journal-length paper which has an overarching purpose to synthesize the literature in a field, without collecting or analyzing any primary data ( Green, Johnson, & Adams, 2006 ).

When appropriately conducted, review articles represent powerful information sources for practitioners looking for state-of-the art evidence to guide their decision-making and work practices ( Paré et al., 2015 ). Further, high-quality reviews become frequently cited pieces of work which researchers seek out as a first clear outline of the literature when undertaking empirical studies ( Cooper, 1988 ; Rowe, 2014 ). Scholars who track and gauge the impact of articles have found that review papers are cited and downloaded more often than any other type of published article ( Cronin, Ryan, & Coughlan, 2008 ; Montori, Wilczynski, Morgan, Haynes, & Hedges, 2003 ; Patsopoulos, Analatos, & Ioannidis, 2005 ). The reason for their popularity may be the fact that reading the review enables one to have an overview, if not a detailed knowledge of the area in question, as well as references to the most useful primary sources ( Cronin et al., 2008 ). Although they are not easy to conduct, the commitment to complete a review article provides a tremendous service to one’s academic community ( Paré et al., 2015 ; Petticrew & Roberts, 2006 ). Most, if not all, peer-reviewed journals in the fields of medical informatics publish review articles of some type.

The main objectives of this chapter are fourfold: (a) to provide an overview of the major steps and activities involved in conducting a stand-alone literature review; (b) to describe and contrast the different types of review articles that can contribute to the eHealth knowledge base; (c) to illustrate each review type with one or two examples from the eHealth literature; and (d) to provide a series of recommendations for prospective authors of review articles in this domain.

9.2. Overview of the Literature Review Process and Steps

As explained in Templier and Paré (2015) , there are six generic steps involved in conducting a review article:

• formulating the research question(s) and objective(s),
• searching the extant literature,
• screening for inclusion,
• assessing the quality of primary studies,
• extracting data, and
• analyzing data.

Although these steps are presented here in sequential order, one must keep in mind that the review process can be iterative and that many activities can be initiated during the planning stage and later refined during subsequent phases ( Finfgeld-Connett & Johnson, 2013 ; Kitchenham & Charters, 2007 ).

Formulating the research question(s) and objective(s): As a first step, members of the review team must appropriately justify the need for the review itself ( Petticrew & Roberts, 2006 ), identify the review’s main objective(s) ( Okoli & Schabram, 2010 ), and define the concepts or variables at the heart of their synthesis ( Cooper & Hedges, 2009 ; Webster & Watson, 2002 ). Importantly, they also need to articulate the research question(s) they propose to investigate ( Kitchenham & Charters, 2007 ). In this regard, we concur with Jesson, Matheson, and Lacey (2011) that clearly articulated research questions are key ingredients that guide the entire review methodology; they underscore the type of information that is needed, inform the search for and selection of relevant literature, and guide or orient the subsequent analysis. Searching the extant literature: The next step consists of searching the literature and making decisions about the suitability of material to be considered in the review ( Cooper, 1988 ). There exist three main coverage strategies. First, exhaustive coverage means an effort is made to be as comprehensive as possible in order to ensure that all relevant studies, published and unpublished, are included in the review and, thus, conclusions are based on this all-inclusive knowledge base. The second type of coverage consists of presenting materials that are representative of most other works in a given field or area. Often authors who adopt this strategy will search for relevant articles in a small number of top-tier journals in a field ( Paré et al., 2015 ). In the third strategy, the review team concentrates on prior works that have been central or pivotal to a particular topic. This may include empirical studies or conceptual papers that initiated a line of investigation, changed how problems or questions were framed, introduced new methods or concepts, or engendered important debate ( Cooper, 1988 ). Screening for inclusion: The following step consists of evaluating the applicability of the material identified in the preceding step ( Levy & Ellis, 2006 ; vom Brocke et al., 2009 ). Once a group of potential studies has been identified, members of the review team must screen them to determine their relevance ( Petticrew & Roberts, 2006 ). A set of predetermined rules provides a basis for including or excluding certain studies. This exercise requires a significant investment on the part of researchers, who must ensure enhanced objectivity and avoid biases or mistakes. As discussed later in this chapter, for certain types of reviews there must be at least two independent reviewers involved in the screening process and a procedure to resolve disagreements must also be in place ( Liberati et al., 2009 ; Shea et al., 2009 ). Assessing the quality of primary studies: In addition to screening material for inclusion, members of the review team may need to assess the scientific quality of the selected studies, that is, appraise the rigour of the research design and methods. Such formal assessment, which is usually conducted independently by at least two coders, helps members of the review team refine which studies to include in the final sample, determine whether or not the differences in quality may affect their conclusions, or guide how they analyze the data and interpret the findings ( Petticrew & Roberts, 2006 ). Ascribing quality scores to each primary study or considering through domain-based evaluations which study components have or have not been designed and executed appropriately makes it possible to reflect on the extent to which the selected study addresses possible biases and maximizes validity ( Shea et al., 2009 ). Extracting data: The following step involves gathering or extracting applicable information from each primary study included in the sample and deciding what is relevant to the problem of interest ( Cooper & Hedges, 2009 ). Indeed, the type of data that should be recorded mainly depends on the initial research questions ( Okoli & Schabram, 2010 ). However, important information may also be gathered about how, when, where and by whom the primary study was conducted, the research design and methods, or qualitative/quantitative results ( Cooper & Hedges, 2009 ). Analyzing and synthesizing data : As a final step, members of the review team must collate, summarize, aggregate, organize, and compare the evidence extracted from the included studies. The extracted data must be presented in a meaningful way that suggests a new contribution to the extant literature ( Jesson et al., 2011 ). Webster and Watson (2002) warn researchers that literature reviews should be much more than lists of papers and should provide a coherent lens to make sense of extant knowledge on a given topic. There exist several methods and techniques for synthesizing quantitative (e.g., frequency analysis, meta-analysis) and qualitative (e.g., grounded theory, narrative analysis, meta-ethnography) evidence ( Dixon-Woods, Agarwal, Jones, Young, & Sutton, 2005 ; Thomas & Harden, 2008 ).

9.3. Types of Review Articles and Brief Illustrations

EHealth researchers have at their disposal a number of approaches and methods for making sense out of existing literature, all with the purpose of casting current research findings into historical contexts or explaining contradictions that might exist among a set of primary research studies conducted on a particular topic. Our classification scheme is largely inspired from Paré and colleagues’ (2015) typology. Below we present and illustrate those review types that we feel are central to the growth and development of the eHealth domain.

9.3.1. Narrative Reviews

The narrative review is the “traditional” way of reviewing the extant literature and is skewed towards a qualitative interpretation of prior knowledge ( Sylvester et al., 2013 ). Put simply, a narrative review attempts to summarize or synthesize what has been written on a particular topic but does not seek generalization or cumulative knowledge from what is reviewed ( Davies, 2000 ; Green et al., 2006 ). Instead, the review team often undertakes the task of accumulating and synthesizing the literature to demonstrate the value of a particular point of view ( Baumeister & Leary, 1997 ). As such, reviewers may selectively ignore or limit the attention paid to certain studies in order to make a point. In this rather unsystematic approach, the selection of information from primary articles is subjective, lacks explicit criteria for inclusion and can lead to biased interpretations or inferences ( Green et al., 2006 ). There are several narrative reviews in the particular eHealth domain, as in all fields, which follow such an unstructured approach ( Silva et al., 2015 ; Paul et al., 2015 ).

Despite these criticisms, this type of review can be very useful in gathering together a volume of literature in a specific subject area and synthesizing it. As mentioned above, its primary purpose is to provide the reader with a comprehensive background for understanding current knowledge and highlighting the significance of new research ( Cronin et al., 2008 ). Faculty like to use narrative reviews in the classroom because they are often more up to date than textbooks, provide a single source for students to reference, and expose students to peer-reviewed literature ( Green et al., 2006 ). For researchers, narrative reviews can inspire research ideas by identifying gaps or inconsistencies in a body of knowledge, thus helping researchers to determine research questions or formulate hypotheses. Importantly, narrative reviews can also be used as educational articles to bring practitioners up to date with certain topics of issues ( Green et al., 2006 ).

Recently, there have been several efforts to introduce more rigour in narrative reviews that will elucidate common pitfalls and bring changes into their publication standards. Information systems researchers, among others, have contributed to advancing knowledge on how to structure a “traditional” review. For instance, Levy and Ellis (2006) proposed a generic framework for conducting such reviews. Their model follows the systematic data processing approach comprised of three steps, namely: (a) literature search and screening; (b) data extraction and analysis; and (c) writing the literature review. They provide detailed and very helpful instructions on how to conduct each step of the review process. As another methodological contribution, vom Brocke et al. (2009) offered a series of guidelines for conducting literature reviews, with a particular focus on how to search and extract the relevant body of knowledge. Last, Bandara, Miskon, and Fielt (2011) proposed a structured, predefined and tool-supported method to identify primary studies within a feasible scope, extract relevant content from identified articles, synthesize and analyze the findings, and effectively write and present the results of the literature review. We highly recommend that prospective authors of narrative reviews consult these useful sources before embarking on their work.

Darlow and Wen (2015) provide a good example of a highly structured narrative review in the eHealth field. These authors synthesized published articles that describe the development process of mobile health ( m-health ) interventions for patients’ cancer care self-management. As in most narrative reviews, the scope of the research questions being investigated is broad: (a) how development of these systems are carried out; (b) which methods are used to investigate these systems; and (c) what conclusions can be drawn as a result of the development of these systems. To provide clear answers to these questions, a literature search was conducted on six electronic databases and Google Scholar . The search was performed using several terms and free text words, combining them in an appropriate manner. Four inclusion and three exclusion criteria were utilized during the screening process. Both authors independently reviewed each of the identified articles to determine eligibility and extract study information. A flow diagram shows the number of studies identified, screened, and included or excluded at each stage of study selection. In terms of contributions, this review provides a series of practical recommendations for m-health intervention development.

9.3.2. Descriptive or Mapping Reviews

The primary goal of a descriptive review is to determine the extent to which a body of knowledge in a particular research topic reveals any interpretable pattern or trend with respect to pre-existing propositions, theories, methodologies or findings ( King & He, 2005 ; Paré et al., 2015 ). In contrast with narrative reviews, descriptive reviews follow a systematic and transparent procedure, including searching, screening and classifying studies ( Petersen, Vakkalanka, & Kuzniarz, 2015 ). Indeed, structured search methods are used to form a representative sample of a larger group of published works ( Paré et al., 2015 ). Further, authors of descriptive reviews extract from each study certain characteristics of interest, such as publication year, research methods, data collection techniques, and direction or strength of research outcomes (e.g., positive, negative, or non-significant) in the form of frequency analysis to produce quantitative results ( Sylvester et al., 2013 ). In essence, each study included in a descriptive review is treated as the unit of analysis and the published literature as a whole provides a database from which the authors attempt to identify any interpretable trends or draw overall conclusions about the merits of existing conceptualizations, propositions, methods or findings ( Paré et al., 2015 ). In doing so, a descriptive review may claim that its findings represent the state of the art in a particular domain ( King & He, 2005 ).

In the fields of health sciences and medical informatics, reviews that focus on examining the range, nature and evolution of a topic area are described by Anderson, Allen, Peckham, and Goodwin (2008) as mapping reviews . Like descriptive reviews, the research questions are generic and usually relate to publication patterns and trends. There is no preconceived plan to systematically review all of the literature although this can be done. Instead, researchers often present studies that are representative of most works published in a particular area and they consider a specific time frame to be mapped.

An example of this approach in the eHealth domain is offered by DeShazo, Lavallie, and Wolf (2009). The purpose of this descriptive or mapping review was to characterize publication trends in the medical informatics literature over a 20-year period (1987 to 2006). To achieve this ambitious objective, the authors performed a bibliometric analysis of medical informatics citations indexed in medline using publication trends, journal frequencies, impact factors, Medical Subject Headings (MeSH) term frequencies, and characteristics of citations. Findings revealed that there were over 77,000 medical informatics articles published during the covered period in numerous journals and that the average annual growth rate was 12%. The MeSH term analysis also suggested a strong interdisciplinary trend. Finally, average impact scores increased over time with two notable growth periods. Overall, patterns in research outputs that seem to characterize the historic trends and current components of the field of medical informatics suggest it may be a maturing discipline (DeShazo et al., 2009).

9.3.3. Scoping Reviews

Scoping reviews attempt to provide an initial indication of the potential size and nature of the extant literature on an emergent topic (Arksey & O’Malley, 2005; Daudt, van Mossel, & Scott, 2013 ; Levac, Colquhoun, & O’Brien, 2010). A scoping review may be conducted to examine the extent, range and nature of research activities in a particular area, determine the value of undertaking a full systematic review (discussed next), or identify research gaps in the extant literature ( Paré et al., 2015 ). In line with their main objective, scoping reviews usually conclude with the presentation of a detailed research agenda for future works along with potential implications for both practice and research.

Unlike narrative and descriptive reviews, the whole point of scoping the field is to be as comprehensive as possible, including grey literature (Arksey & O’Malley, 2005). Inclusion and exclusion criteria must be established to help researchers eliminate studies that are not aligned with the research questions. It is also recommended that at least two independent coders review abstracts yielded from the search strategy and then the full articles for study selection ( Daudt et al., 2013 ). The synthesized evidence from content or thematic analysis is relatively easy to present in tabular form (Arksey & O’Malley, 2005; Thomas & Harden, 2008 ).

One of the most highly cited scoping reviews in the eHealth domain was published by Archer, Fevrier-Thomas, Lokker, McKibbon, and Straus (2011) . These authors reviewed the existing literature on personal health record ( phr ) systems including design, functionality, implementation, applications, outcomes, and benefits. Seven databases were searched from 1985 to March 2010. Several search terms relating to phr s were used during this process. Two authors independently screened titles and abstracts to determine inclusion status. A second screen of full-text articles, again by two independent members of the research team, ensured that the studies described phr s. All in all, 130 articles met the criteria and their data were extracted manually into a database. The authors concluded that although there is a large amount of survey, observational, cohort/panel, and anecdotal evidence of phr benefits and satisfaction for patients, more research is needed to evaluate the results of phr implementations. Their in-depth analysis of the literature signalled that there is little solid evidence from randomized controlled trials or other studies through the use of phr s. Hence, they suggested that more research is needed that addresses the current lack of understanding of optimal functionality and usability of these systems, and how they can play a beneficial role in supporting patient self-management ( Archer et al., 2011 ).

9.3.4. Forms of Aggregative Reviews

Healthcare providers, practitioners, and policy-makers are nowadays overwhelmed with large volumes of information, including research-based evidence from numerous clinical trials and evaluation studies, assessing the effectiveness of health information technologies and interventions ( Ammenwerth & de Keizer, 2004 ; Deshazo et al., 2009 ). It is unrealistic to expect that all these disparate actors will have the time, skills, and necessary resources to identify the available evidence in the area of their expertise and consider it when making decisions. Systematic reviews that involve the rigorous application of scientific strategies aimed at limiting subjectivity and bias (i.e., systematic and random errors) can respond to this challenge.

Systematic reviews attempt to aggregate, appraise, and synthesize in a single source all empirical evidence that meet a set of previously specified eligibility criteria in order to answer a clearly formulated and often narrow research question on a particular topic of interest to support evidence-based practice ( Liberati et al., 2009 ). They adhere closely to explicit scientific principles ( Liberati et al., 2009 ) and rigorous methodological guidelines (Higgins & Green, 2008) aimed at reducing random and systematic errors that can lead to deviations from the truth in results or inferences. The use of explicit methods allows systematic reviews to aggregate a large body of research evidence, assess whether effects or relationships are in the same direction and of the same general magnitude, explain possible inconsistencies between study results, and determine the strength of the overall evidence for every outcome of interest based on the quality of included studies and the general consistency among them ( Cook, Mulrow, & Haynes, 1997 ). The main procedures of a systematic review involve:

• Formulating a review question and developing a search strategy based on explicit inclusion criteria for the identification of eligible studies (usually described in the context of a detailed review protocol).
• Searching for eligible studies using multiple databases and information sources, including grey literature sources, without any language restrictions.
• Selecting studies, extracting data, and assessing risk of bias in a duplicate manner using two independent reviewers to avoid random or systematic errors in the process.
• Analyzing data using quantitative or qualitative methods.
• Presenting results in summary of findings tables.
• Interpreting results and drawing conclusions.

Many systematic reviews, but not all, use statistical methods to combine the results of independent studies into a single quantitative estimate or summary effect size. Known as meta-analyses , these reviews use specific data extraction and statistical techniques (e.g., network, frequentist, or Bayesian meta-analyses) to calculate from each study by outcome of interest an effect size along with a confidence interval that reflects the degree of uncertainty behind the point estimate of effect ( Borenstein, Hedges, Higgins, & Rothstein, 2009 ; Deeks, Higgins, & Altman, 2008 ). Subsequently, they use fixed or random-effects analysis models to combine the results of the included studies, assess statistical heterogeneity, and calculate a weighted average of the effect estimates from the different studies, taking into account their sample sizes. The summary effect size is a value that reflects the average magnitude of the intervention effect for a particular outcome of interest or, more generally, the strength of a relationship between two variables across all studies included in the systematic review. By statistically combining data from multiple studies, meta-analyses can create more precise and reliable estimates of intervention effects than those derived from individual studies alone, when these are examined independently as discrete sources of information.

The review by Gurol-Urganci, de Jongh, Vodopivec-Jamsek, Atun, and Car (2013) on the effects of mobile phone messaging reminders for attendance at healthcare appointments is an illustrative example of a high-quality systematic review with meta-analysis. Missed appointments are a major cause of inefficiency in healthcare delivery with substantial monetary costs to health systems. These authors sought to assess whether mobile phone-based appointment reminders delivered through Short Message Service ( sms ) or Multimedia Messaging Service ( mms ) are effective in improving rates of patient attendance and reducing overall costs. To this end, they conducted a comprehensive search on multiple databases using highly sensitive search strategies without language or publication-type restrictions to identify all rct s that are eligible for inclusion. In order to minimize the risk of omitting eligible studies not captured by the original search, they supplemented all electronic searches with manual screening of trial registers and references contained in the included studies. Study selection, data extraction, and risk of bias assessments were performed inde­­pen­dently by two coders using standardized methods to ensure consistency and to eliminate potential errors. Findings from eight rct s involving 6,615 participants were pooled into meta-analyses to calculate the magnitude of effects that mobile text message reminders have on the rate of attendance at healthcare appointments compared to no reminders and phone call reminders.

Meta-analyses are regarded as powerful tools for deriving meaningful conclusions. However, there are situations in which it is neither reasonable nor appropriate to pool studies together using meta-analytic methods simply because there is extensive clinical heterogeneity between the included studies or variation in measurement tools, comparisons, or outcomes of interest. In these cases, systematic reviews can use qualitative synthesis methods such as vote counting, content analysis, classification schemes and tabulations, as an alternative approach to narratively synthesize the results of the independent studies included in the review. This form of review is known as qualitative systematic review.

A rigorous example of one such review in the eHealth domain is presented by Mickan, Atherton, Roberts, Heneghan, and Tilson (2014) on the use of handheld computers by healthcare professionals and their impact on access to information and clinical decision-making. In line with the methodological guide­lines for systematic reviews, these authors: (a) developed and registered with prospero ( www.crd.york.ac.uk/ prospero / ) an a priori review protocol; (b) conducted comprehensive searches for eligible studies using multiple databases and other supplementary strategies (e.g., forward searches); and (c) subsequently carried out study selection, data extraction, and risk of bias assessments in a duplicate manner to eliminate potential errors in the review process. Heterogeneity between the included studies in terms of reported outcomes and measures precluded the use of meta-analytic methods. To this end, the authors resorted to using narrative analysis and synthesis to describe the effectiveness of handheld computers on accessing information for clinical knowledge, adherence to safety and clinical quality guidelines, and diagnostic decision-making.

In recent years, the number of systematic reviews in the field of health informatics has increased considerably. Systematic reviews with discordant findings can cause great confusion and make it difficult for decision-makers to interpret the review-level evidence ( Moher, 2013 ). Therefore, there is a growing need for appraisal and synthesis of prior systematic reviews to ensure that decision-making is constantly informed by the best available accumulated evidence. Umbrella reviews , also known as overviews of systematic reviews, are tertiary types of evidence synthesis that aim to accomplish this; that is, they aim to compare and contrast findings from multiple systematic reviews and meta-analyses ( Becker & Oxman, 2008 ). Umbrella reviews generally adhere to the same principles and rigorous methodological guidelines used in systematic reviews. However, the unit of analysis in umbrella reviews is the systematic review rather than the primary study ( Becker & Oxman, 2008 ). Unlike systematic reviews that have a narrow focus of inquiry, umbrella reviews focus on broader research topics for which there are several potential interventions ( Smith, Devane, Begley, & Clarke, 2011 ). A recent umbrella review on the effects of home telemonitoring interventions for patients with heart failure critically appraised, compared, and synthesized evidence from 15 systematic reviews to investigate which types of home telemonitoring technologies and forms of interventions are more effective in reducing mortality and hospital admissions ( Kitsiou, Paré, & Jaana, 2015 ).

9.3.5. Realist Reviews

Realist reviews are theory-driven interpretative reviews developed to inform, enhance, or supplement conventional systematic reviews by making sense of heterogeneous evidence about complex interventions applied in diverse contexts in a way that informs policy decision-making ( Greenhalgh, Wong, Westhorp, & Pawson, 2011 ). They originated from criticisms of positivist systematic reviews which centre on their “simplistic” underlying assumptions ( Oates, 2011 ). As explained above, systematic reviews seek to identify causation. Such logic is appropriate for fields like medicine and education where findings of randomized controlled trials can be aggregated to see whether a new treatment or intervention does improve outcomes. However, many argue that it is not possible to establish such direct causal links between interventions and outcomes in fields such as social policy, management, and information systems where for any intervention there is unlikely to be a regular or consistent outcome ( Oates, 2011 ; Pawson, 2006 ; Rousseau, Manning, & Denyer, 2008 ).

To circumvent these limitations, Pawson, Greenhalgh, Harvey, and Walshe (2005) have proposed a new approach for synthesizing knowledge that seeks to unpack the mechanism of how “complex interventions” work in particular contexts. The basic research question — what works? — which is usually associated with systematic reviews changes to: what is it about this intervention that works, for whom, in what circumstances, in what respects and why? Realist reviews have no particular preference for either quantitative or qualitative evidence. As a theory-building approach, a realist review usually starts by articulating likely underlying mechanisms and then scrutinizes available evidence to find out whether and where these mechanisms are applicable ( Shepperd et al., 2009 ). Primary studies found in the extant literature are viewed as case studies which can test and modify the initial theories ( Rousseau et al., 2008 ).

The main objective pursued in the realist review conducted by Otte-Trojel, de Bont, Rundall, and van de Klundert (2014) was to examine how patient portals contribute to health service delivery and patient outcomes. The specific goals were to investigate how outcomes are produced and, most importantly, how variations in outcomes can be explained. The research team started with an exploratory review of background documents and research studies to identify ways in which patient portals may contribute to health service delivery and patient outcomes. The authors identified six main ways which represent “educated guesses” to be tested against the data in the evaluation studies. These studies were identified through a formal and systematic search in four databases between 2003 and 2013. Two members of the research team selected the articles using a pre-established list of inclusion and exclusion criteria and following a two-step procedure. The authors then extracted data from the selected articles and created several tables, one for each outcome category. They organized information to bring forward those mechanisms where patient portals contribute to outcomes and the variation in outcomes across different contexts.

9.3.6. Critical Reviews

Lastly, critical reviews aim to provide a critical evaluation and interpretive analysis of existing literature on a particular topic of interest to reveal strengths, weaknesses, contradictions, controversies, inconsistencies, and/or other important issues with respect to theories, hypotheses, research methods or results ( Baumeister & Leary, 1997 ; Kirkevold, 1997 ). Unlike other review types, critical reviews attempt to take a reflective account of the research that has been done in a particular area of interest, and assess its credibility by using appraisal instruments or critical interpretive methods. In this way, critical reviews attempt to constructively inform other scholars about the weaknesses of prior research and strengthen knowledge development by giving focus and direction to studies for further improvement ( Kirkevold, 1997 ).

Kitsiou, Paré, and Jaana (2013) provide an example of a critical review that assessed the methodological quality of prior systematic reviews of home telemonitoring studies for chronic patients. The authors conducted a comprehensive search on multiple databases to identify eligible reviews and subsequently used a validated instrument to conduct an in-depth quality appraisal. Results indicate that the majority of systematic reviews in this particular area suffer from important methodological flaws and biases that impair their internal validity and limit their usefulness for clinical and decision-making purposes. To this end, they provide a number of recommendations to strengthen knowledge development towards improving the design and execution of future reviews on home telemonitoring.

9.4. Summary

Table 9.1 outlines the main types of literature reviews that were described in the previous sub-sections and summarizes the main characteristics that distinguish one review type from another. It also includes key references to methodological guidelines and useful sources that can be used by eHealth scholars and researchers for planning and developing reviews.

Typology of Literature Reviews (adapted from Paré et al., 2015).

As shown in Table 9.1 , each review type addresses different kinds of research questions or objectives, which subsequently define and dictate the methods and approaches that need to be used to achieve the overarching goal(s) of the review. For example, in the case of narrative reviews, there is greater flexibility in searching and synthesizing articles ( Green et al., 2006 ). Researchers are often relatively free to use a diversity of approaches to search, identify, and select relevant scientific articles, describe their operational characteristics, present how the individual studies fit together, and formulate conclusions. On the other hand, systematic reviews are characterized by their high level of systematicity, rigour, and use of explicit methods, based on an “a priori” review plan that aims to minimize bias in the analysis and synthesis process (Higgins & Green, 2008). Some reviews are exploratory in nature (e.g., scoping/mapping reviews), whereas others may be conducted to discover patterns (e.g., descriptive reviews) or involve a synthesis approach that may include the critical analysis of prior research ( Paré et al., 2015 ). Hence, in order to select the most appropriate type of review, it is critical to know before embarking on a review project, why the research synthesis is conducted and what type of methods are best aligned with the pursued goals.

9.5. Concluding Remarks

In light of the increased use of evidence-based practice and research generating stronger evidence ( Grady et al., 2011 ; Lyden et al., 2013 ), review articles have become essential tools for summarizing, synthesizing, integrating or critically appraising prior knowledge in the eHealth field. As mentioned earlier, when rigorously conducted review articles represent powerful information sources for eHealth scholars and practitioners looking for state-of-the-art evidence. The typology of literature reviews we used herein will allow eHealth researchers, graduate students and practitioners to gain a better understanding of the similarities and differences between review types.

We must stress that this classification scheme does not privilege any specific type of review as being of higher quality than another ( Paré et al., 2015 ). As explained above, each type of review has its own strengths and limitations. Having said that, we realize that the methodological rigour of any review — be it qualitative, quantitative or mixed — is a critical aspect that should be considered seriously by prospective authors. In the present context, the notion of rigour refers to the reliability and validity of the review process described in section 9.2. For one thing, reliability is related to the reproducibility of the review process and steps, which is facilitated by a comprehensive documentation of the literature search process, extraction, coding and analysis performed in the review. Whether the search is comprehensive or not, whether it involves a methodical approach for data extraction and synthesis or not, it is important that the review documents in an explicit and transparent manner the steps and approach that were used in the process of its development. Next, validity characterizes the degree to which the review process was conducted appropriately. It goes beyond documentation and reflects decisions related to the selection of the sources, the search terms used, the period of time covered, the articles selected in the search, and the application of backward and forward searches ( vom Brocke et al., 2009 ). In short, the rigour of any review article is reflected by the explicitness of its methods (i.e., transparency) and the soundness of the approach used. We refer those interested in the concepts of rigour and quality to the work of Templier and Paré (2015) which offers a detailed set of methodological guidelines for conducting and evaluating various types of review articles.

To conclude, our main objective in this chapter was to demystify the various types of literature reviews that are central to the continuous development of the eHealth field. It is our hope that our descriptive account will serve as a valuable source for those conducting, evaluating or using reviews in this important and growing domain.

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• Cite this Page Paré G, Kitsiou S. Chapter 9 Methods for Literature Reviews. In: Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.
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• Introduction
• Overview of the Literature Review Process and Steps
• Types of Review Articles and Brief Illustrations
• Concluding Remarks

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Assess Evidence and Build a Table

One of the most important steps in writing a paper is showing the strength and rationale of the evidence you chosen.  The following document discusses the reasoning, grading and creation of a "Table of Evidence."  While tables of evidence can differ, the examples given in this article are a great starting point.

Sample table

A standard evidence table has 10 columns. You need to fill in all of the columns for each study you find to properly review the available evidence. Evidence tables are often included in Systematic reviews and represent a great tool in taking evidence-based practice from the page into the clinical setting, especially, when you are making an administrative change in the treatment of patients.

Column 1: Condition

This column refers to the medical condition or disease that is targeted by the therapy.

Column 2: Study Design

This column is where you report the type of study you found. Review (Section) for more information on these study types and the quality of evidence they represent.

List one of the following

• Randomized controlled trial (RCT)
• Equivalence trial: An RCT which compares two active agents. Equivalence trials often compare new treatments to usual (standard) care, and may not include a placebo arm.
• Before and after comparison: A study that reports only the change in outcome in each group of a study, and does not report between-group comparisons. This is a common error in studies that claim to be RCTs.
• Case series
• Case Report
• Case-control study
• Cohort study
• Meta-analysis
• Systematic review
• P:Pending verification

Column 3: Author, Year

Identifies Author and Year of the study being described in a row of the table.

Column 4: N

The total number of subjects included in a study (treatment group plus placebo group). Some studies recruit a larger number of subjects initially, but do not use them all because they do not meet the study's entry criteria. In this case, it is the second, smaller number that qualifies as N.

Trials with a large number of drop-outs that are not included in the analysis are considered to be weaker evidence for efficacy. (For systematic reviews the number of studies included is reported. For meta-analyses, the number of total subjects included in the analysis or the number of studies may be reported.)

P= pending verification.

Column 5: Statistically Significant?

Results are noted as being statistically significant if a study's authors report statistical significance, or if quantitative evidence of significance is present.

Column 6: Quality of Study

A numerical score between 0-5 is assigned as a rough measure of study design/reporting quality (0 being weakest and 5 being strongest). This number is based on a well-established, validated scale developed by Jadad et al. (Jadad AR, Moore RA, Carroll D, et al. Assessing the quality of reports of randomized clinical trials: is blinding necessary?  Controlled Clinical Trials  1996;17[1]:1-12).

Column 7: Magnitude of Benefit

This summarizes how strong a benefit is: small, medium, large, or none. If results are not statistically significant "NA" for "not applicable" is entered. Be consistent in defining small, medium, and large benefits across different studies and monographs

NA= not applicable.

Column 8: Absolute Risk Reduction

This describes the difference between the percent of people in the control/placebo group experiencing a specific outcome (control event rate), and the percent of people in the experimental/therapy group experiencing that same outcome (experimental event rate). Mathematically, Absolute risk reduction (ARR) equals experimental event rate minus control event rate.

Many studies do not include adequate data to calculate the ARR, in which cases "NA" is entered into this column.

Column 9: Number Needed to Treat

This is the number of patients who would need to use the therapy under investigation, for the period of time described in the study, in order for one person to experience the specified benefit. It is calculated by dividing the Absolute Risk Reduction into 1 (1/ARR).

Column 10: Comments

When appropriate, this brief section may comment on design flaws (inadequately described subjects, lack of blinding, brief follow-up, not intention-to treat, etc.), notable study design elements (crossover, etc.), dosing, and/or specifics of study group/sub-groups (age, gender, etc).

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• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

Scribbr slides are free to use, customize, and distribute for educational purposes.

Open Google Slides Download PowerPoint

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

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Building a Summary Table or Synthesis Matrix

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• Simplifying Synthesis | Download the Article PDF Copy
• Writing a Literature Review and Using a Synthesis Matrix

What a Summary Table or Synthesis Matrix looks like

Use the "Literature Review Matrix Template" as a guideline to help you sort through your thoughts, note important points and think through the similarities and differences: 

You are organizing the review by ideas and not by sources .  The literature review is not just a summary of the already published works.  Your synthesis should show how various articles are linked. 

A summary table is also called a synthesis matrix.  The table helps you organize and compare information for your systematic review, scholarly report, dissertation or thesis

Synthesis Matrix.

A summary table is also called a synthesis matrix . A summary table helps you record the main points of each source and document how sources relate to each other. After summarizing and evaluating your sources, arrange them in a matrix to help you see how they relate to each other, and apply to each of your themes or variables.

Faculty who typically guide students find it challenging to help students learn how to synthesize material (Blondy, Blakesless, Scheffer, Rubenfeld, Cronin, & Luster-Turner, 2016; Kearney, 2015) .  Writers  can easily summarize material but seem to struggle to adequately synthesize knowledge about their topic and express that in their writing. So, whether you are writing a student papers, dissertations, or scholarly report it is necessary to learn a few tips and tricks to organize your ideas.

Building a summary table and developing solid synthesis skills is important for nurses, nurse practitioners, and allied health researchers.  Quality evidence-based practice initiatives and nursing care and medicine are based on understanding and evaluating the resources and research available, identifying gaps, and building a strong foundation for future work.

Good synthesis is about putting the data gathered, references read, and literature analyzed together in a new way that shows connections and relationships. ( Shellenbarger, 2016 ). The Merriam-Webster dictionary defines synthesis as something that is made by combining different things or the composition or combination of parts or elements so as to form a whole (Synthesis, n.d.).  

In other words, building a summary table or synthesis matrix  involves taking information from a variety of sources, evaluating that information and forming new ideas or insights in an original way.  This can be a new and potentially challenging experience for students and researchers who are used to just repeating what is already in the literature.

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Literature Reviews: Systematic, Scoping, Integrative

• Introduction to Literature Reviews
• Creating a Search Strategy
• Limits and Inclusion Criteria
• Review Protocols

Elements of a Systematic Review

• Methods Section

PRISMA Flow Diagram

Data extraction and evidence tables, search strategy table.

• Review Tools and Applications
• Writing a Literature Review

This section of the guide will not address  every  element of formal literature reviews but some of those that are unique to this type of research and those that seem to engender the most questions.

Methods Section 

The methods section of any formal review should include enough detail that a reader could reproduce the search faithfully and follow the same process you used to review articles for inclusion. Here is a list of elements it should include.

• Names of the databases you searched, including the database platform and the coverage dates
• Whether you will be mining reference lists for additional sources
• Grey literature sources, including how you identified them and whether there were differences in the search techniques
• Your search terms ,  differentiating between keywords and subject headings. You can make an exception to this if you include your search terms in an appendix, in which case you might provide a summary of your terms in the body of the methods section (see the Search Strategy Table section for more details). At this point, you would also cite any methods you used to review your search strategy.
• The date you conducted the search. A month, day, and year is ideal.
• Filters or limits you applied to the search (e.g. limited to English language articles).
• Inclusion/exclusion criteria

The results section provides details about your search results, your process for reviewing those results, and the evidence you extracted from the results. It will include the following elements.

• Number of articles, etc. retrieved. Report those numbers separately based on the source: databases (including nu mber of duplicates removed), clinical trial registries, reference lists, and grey literature. You may not have articles from all of these sources, depending on the scope of your review.
• The number of articles you excluded during each round of screening based on your inclusion/exclusion criteria.
• PRISMA Flow Diagram .
• Did you use Covidence or another application?
• How many people reviewed each source?
• How many people were involved
• What method did you use (Covidence, spreadsheet, etc.)
• Summary list of the data points you extracted
• Data or evidence table, displaying the data you extracted.

A PRISMA Diagram is a flow chart that shows the decision making path you took to eliminate search results and land on a final group of information sources to review. The content can vary based on the type of review and the types of sources you choose to include. Here is an example

For pdf or Word templates, visit the PRISMA website .

PRISMA flow diagrams can also be generated using a Shiny App available at  https://www.eshackathon.org/software/PRISMA2020.html

For more information, see  Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J.M., Hrobjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S.,...Moher, D. (2021).  The PRISMA 2020 statement: an updated guideline for reporting systematic reviews . PLoS Medicine, 18 (3), Article e1003583. https://doi.org/10.1371/journal.pmed.1003583 

Data extraction is the process of taking the most important characteristics of each study in the review and placing them into a chart, so that the details can be easily compared across studies. Typically, at least two people independently extract the data and then compare the results afterward, resolving any conflicts in the data that may have arisen in the process. Their data is them combined to create one final table. 

While there are no prescribed set of study characteristics to include in a review, the most frequently extracted data items are things like:

• Authors and their affiliations
• Publication date
• Geographic location of the study
• Study design details
• Aims/objectives of the study
• Population studied
• Major findings
• Implications

There are many different ways to extract data. You can use a form that you build in Microsoft Forms, Google Forms, RedCap or in a tool like Covidence. (See the Review Tools and Applications tab.) You can enter the data directly into a spreadsheet program like Excel and then blend the charts later by copying and pasting. No matter how you extract the data, the important thing is to describe how you did it in the methods section, and to reduce bias in the results by having the extractors work independently.

You may also want to pilot your data extraction tool. To do this, have your extractors work on 2-3 articles to start. Compare answers and ensure that the results are similar. If your tool is lacking interrater reliability, it may be a sign that the extractors aren't understanding the data items the same way, and that definitions may need to be clarified.

Examples of Evidence Tables

• NICE Examples of evidence tables The National Institute for Health and Care Excellence provides examples of evidence tables for both quantitative and qualitative studies, with definitions for each data item.
• Completing ‘Summary of findings’ tables and grading the certainty of the evidence The Cochrane Handbook provides guidance for choosing data items to extract and for building the evidence table.

Depending on the complexity of your search and how many terms it contains, you may not want to include all of the details in the text of the Methods section of your paper. In that case you might opt for an appendix or other supplemental material, depending on what the journal you're submitting to allows.

There are several ways you might format the search strategy. In Cochrane reviews, the terms are simply laid out in a list for each database searched (see Appendix 1 in this Cochrane review ). 

You could also opt for a table that implies the relationship between the search terms, like this one:

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Literature Review Basics

• What is a Literature Review?
• Synthesizing Research
• Using Research & Synthesis Tables
• Additional Resources

About the Research and Synthesis Tables

Research Tables and Synthesis Tables are useful tools for organizing and analyzing your research as you assemble your literature review. They represent two different parts of the review process: assembling relevant information and synthesizing it. Use a Research table to compile the main info you need about the items you find in your research -- it's a great thing to have on hand as you take notes on what you read! Then, once you've assembled your research, use the Synthesis table to start charting the similarities/differences and major themes among your collected items.

We've included an Excel file with templates for you to use below; the examples pictured on this page are snapshots from that file.

• Research and Synthesis Table Templates This Excel workbook includes simple templates for creating research tables and synthesis tables. Feel free to download and use!

Using the Research Table

This is an example of a  research table,  in which you provide a basic description of the most important features of the studies, articles, and other items you discover in your research. The table identifies each item according to its author/date of publication, its purpose or thesis, what type of work it is (systematic review, clinical trial, etc.), the level of evidence it represents (which tells you a lot about its impact on the field of study), and its major findings. Your job, when you assemble this information, is to develop a snapshot of what the research shows about the topic of your research question and assess its value (both for the purpose of your work and for general knowledge in the field).

Think of your work on the research table as the foundational step for your analysis of the literature, in which you assemble the information you'll be analyzing and lay the groundwork for thinking about what it means and how it can be used.

Using the Synthesis Table

This is an example of a  synthesis table  or  synthesis matrix , in which you organize and analyze your research by listing each source and indicating whether a given finding or result occurred in a particular study or article ( each row lists an individual source, and each finding has its own column, in which X = yes, blank = no). You can also add or alter the columns to look for shared study populations, sort by level of evidence or source type, etc. The key here is to use the table to provide a simple representation of what the research has found (or not found, as the case may be). Think of a synthesis table as a tool for making comparisons, identifying trends, and locating gaps in the literature.

How do I know which findings to use, or how many to include?  Your research question tells you which findings are of interest in your research, so work from your research question to decide what needs to go in each Finding header, and how many findings are necessary. The number is up to you; again, you can alter this table by adding or deleting columns to match what you're actually looking for in your analysis. You should also, of course, be guided by what's actually present in the material your research turns up!

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PREPUBLICATION COPY: UNCORRECTED PROOFS

Nursing-Johns Hopkins Evidence-Based Practice Model

Jhebp model for levels of evidence, jhebp levels of evidence overview.

• Levels I, II and III

Evidence-Based Practice (EBP) uses a rating system to appraise evidence (usually a research study published as a journal article). The level of evidence corresponds to the research study design. Scientific research is considered to be the strongest form of evidence and recommendations from the strongest form of evidence will most likely lead to the best practices. The strength of evidence can vary from study to study based on the methods used and the quality of reporting by the researchers. You will want to seek the highest level of evidence available on your topic (Dang et al., 2022, p. 130).

The Johns Hopkins EBP model uses 3 ratings for the level of scientific research evidence 

• true experimental (level I)
• quasi-experimental (level II)
• nonexperimental (level III) 

The level determination is based on the research meeting the study design requirements  (Dang et al., 2022, p. 146-7).

You will use the Research Appraisal Tool (Appendix E) along with the Evidence Level and Quality Guide (Appendix D) to analyze and  appraise research studies . (Tools linked below.)

N onresearch evidence is covered in Levels IV and V.

• Evidence Level and Quality Guide (Appendix D)
• Research Evidence Appraisal Tool (Appendix E)

Level I Experimental study

randomized controlled trial (RCT)

Systematic review of RCTs, with or without meta-analysis

Level II Quasi-experimental Study

Systematic review of a combination of RCTs and quasi-experimental, or quasi-experimental studies only, with or without meta-analysis.

Level III Non-experimental study

Systematic review of a combination of RCTs, quasi-experimental and non-experimental, or non-experimental studies only, with or without meta-analysis.

Qualitative study or systematic review, with or without meta-analysis

Level IV Opinion of respected authorities and/or nationally recognized expert committees/consensus panels based on scientific evidence.

Clinical practice guidelines

Consensus panels

Level V Based on experiential and non-research evidence.

Literature reviews

Quality improvement, program, or financial evaluation

Case reports

Opinion of nationally recognized expert(s) based on experiential evidence

These flow charts can also help you detemine the level of evidence throigh a series of questions.

Single Quantitative Research Study

Summary/Reviews 

These charts are a part of the Research Evidence Appraisal Tool (Appendix E) document.

Dang, D., Dearholt, S., Bissett, K., Ascenzi, J., & Whalen, M. (2022). Johns Hopkins evidence-based practice for nurses and healthcare professionals: Model and guidelines. 4th ed. Sigma Theta Tau International

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Methods for the development of NICE public health guidance (third edition)

NICE process and methods [PMG4] Published: 26 September 2012

• 1 Introduction
• 2 Topic selection and scoping the guidance
• 3 Determining the evidence for review and consideration
• 4 Identifying the evidence
• 5 Reviewing the scientific evidence
• 6 Incorporating health economics
• 7 Developing recommendations
• Appendix A Conceptual framework for the work of the Centre for Public Health Excellence (CPHE)
• Appendix B Electronic resources
• Appendix C Example of audit information to accompany search strategies
• Appendix D Glossary of study designs
• Appendix E Algorithm for classifying quantitative (experimental and observational) study designs
• Appendix F Quality appraisal checklist – quantitative intervention studies
• Appendix G Quality appraisal checklist – quantitative studies reporting correlations and associations
• Appendix H Quality appraisal checklist – qualitative studies
• Appendix I Quality appraisal checklist – economic evaluations
• Appendix J Process for using review-level material in exceptional circumstances

Appendix K Examples of evidence tables

• Appendix L NICE review format
• Appendix M Fieldwork
• Appendix N Expert testimony summary template
• Update information
• About this document

NICE process and methods

Confirming format and content with cphe early in the review process, k1 example of what to include in evidence tables for quantitative studies, k2 example of evidence table for qualitative studies, k3 example of evidence table for economic evaluation studies, k4 example of evidence table for review-level material, table footnotes.

Below are examples of the type of information and data NICE requires in table format in evidence reviews. It is not possible to provide a fixed template for all evidence tables that will suit all topics. The range, type, quantity and quality of evidence identified will inevitably vary and these tables are presented as examples only of how information and data should be presented.

It is important that review teams confirm the following with the CPHE project team early in the review process :

The presentation of information and data because it may not exactly replicate that required in a systematic review – this is especially important where it is expected that study information may be used to populate the Effective Interventions Library in NICE Pathways.

Whether additional analysis should be performed on study data for some interventions where sufficient data is reported by the study authors (see chapter 5 ). Where additional calculation (e.g. calculating numbers needed to treat, odds ratios, risk ratios) of data is required and feasible, these must be clearly noted as 'calculated by the review team' (see final column in the tables below for examples).

Please complete for all headings as requested by CPHE and note where data is 'inadequately reported', 'not reported' or 'not applicable'.

Please complete for all headings and note where data is 'Not reported' or 'Not applicable'.

Only report the outcomes that are relevant to the outcomes for the guidance topic under consideration. (NB the study may cover additional outcomes that need not be included here).

Only report the results that are relevant for the guidance topic under consideration.

The internal validity score of a study may vary depending on the reliability and validity of the outcome measures of interest.

Demographic criteria as outlined by the PROGRESS-Plus categorisation p93 chapter 5 methods manual. (Gough D, Oliver S, Thomas J [2012] [eds] An introduction to systematic reviews, London: Sage).

Taken from economic evaluation checklist ( appendix I ).

Only report outcomes that are relevant to the guidance topic under consideration as confirmed with CPHE (NB the review may cover additional outcomes that need not be included here).

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Nursing Literature Reviews

• Literature and Other Types of Reviews
• Starting Your Search
• Developing a Research Question and the Literature Search Process
• Conducting a Literature Search

Levels of Evidence

• Creating a PRISMA Table
• Literature Table and Synthesis
• Other Resources

Levels of evidence (sometimes called hierarchy of evidence) are assigned to studies based on the methodological quality of their design, validity, and applicability to patient care. These decisions gives the grade (or strength) of recommendation. Just because something is lower on the pyramid doesn't mean that the study itself is lower-quality, it just means that the methods used may not be as clinically rigorous as higher levels of the pyramid. In nursing, the system for assigning levels of evidence is often from Melnyk & Fineout-Overholt's 2011 book,  Evidence-based Practice in Nursing and Healthcare: A Guide to Best Practice .  The Levels of Evidence below are adapted from Melnyk & Fineout-Overholt's (2011) model.  

Melnyk & Fineout-Overholt (2011)

• Meta-Analysis:  A systematic review that uses quantitative methods to summarize the results. (Level 1)
• Systematic Review:  A comprehensive review that authors have systematically searched for, appraised, and summarized all of the medical literature for a specific topic (Level 1)
• Randomized Controlled Trials:  RCT's include a randomized group of patients in an experimental group and a control group. These groups are followed up for the variables/outcomes of interest. Examples of RCTs are clinical trials that compare the effects of drugs, surgical techniques, medical devices, diagnostic procedures, diets or other medical treatments. (can be Level 2 or Level 4, depending on how expansive the study)
• Non-Randomized Controlled Trials:  A clinical trial in which the participants are not assigned by chance to different treatment groups. Participants may choose which group they want to be in, or they may be assigned to the groups by the researchers.
• Cohort Study:  Identifies two groups (cohorts) of patients, one which did receive the exposure of interest, and one which did not, and following these cohorts forward for the outcome of interest. ( Level 5)
• Case-Control Study:  Involves identifying patients who have the outcome of interest (cases) and control patients without the same outcome, and looking to see if they had the exposure of interest.
• Background Information/Expert Opinion:  Handbooks, encyclopedias, and textbooks often provide a good foundation or introduction and often include generalized information about a condition.  While background information presents a convenient summary, often it takes about three years for this type of literature to be published. (Level 7)
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One of the most important steps in writing a paper is showing the strength and rationale of the evidence you've chosen.  The following document discusses the reasoning, grading and creation of a "Table of Evidence."  While table of evidences can differ, the example given below can be a starting point. 

This guide (courtesy of Rutgers University Libraries) will introduce you to the Systematic Review process.

The Basics:

A standard evidence table has 10 columns. You need to fill in all of the columns for each study you find to properly review the available evidence. Evidence tables are often included in Systematic reviews and represent a great tool in taking evidence based practice from the page into the clinical setting, especially, when you are making an administrative change in the treatment of patients.

The Ten Columns:

Column 1: Condition

This column refers to the medical condition or disease that is targeted by the therapy.

Column 2: Study Design

This column is where you report the type of study you found. Review (Section) for more information on these study types and the quality of evidence they represent.

List one of the following

• Randomized controlled trial (RCT)
• Equivalence trial: An RCT which compares two active agents. Equivalence trials often compare new treatments to usual (standard) care, and may not include a placebo arm.
• Before and after comparison: A study that reports only the change in outcome in each group of a study, and does not report between-group comparisons. This is a common error in studies that claim to be RCTs.
• Case series
• Case Report
• Case-control study
• Cohort study
• Meta-analysis
• Systematic review
• P:Pending verification

Column 3: Author, Year

Identifies Author and Year of the study being described in a row of the table.

Column 4: N

The total number of subjects included in a study (treatment group plus placebo group). Some studies recruit a larger number of subjects initially, but do not use them all because they do not meet the study's entry criteria. In this case, it is the second, smaller number that qualifies as N.

Trials with a large number of drop-outs that are not included in the analysis are considered to be weaker evidence for efficacy. (For systematic reviews the number of studies included is reported. For meta-analyses, the number of total subjects included in the analysis or the number of studies may be reported.)

P= pending verification.

Column 5: Statistically Significant?

Results are noted as being statistically significant if a study's authors report statistical significance, or if quantitative evidence of significance is present.

Column 6: Quality of Study

A numerical score between 0-5 is assigned as a rough measure of study design/reporting quality (0 being weakest and 5 being strongest). This number is based on a well-established, validated scale developed by Jadad et al. (Jadad AR, Moore RA, Carroll D, et al. Assessing the quality of reports of randomized clinical trials: is blinding necessary?  Controlled Clinical Trials  1996;17[1]:1-12).

Jadad Score Calculation

Column 7: Magnitude of Benefit

This summarizes how strong a benefit is: small, medium, large, or none. If results are not statistically significant "NA" for "not applicable" is entered. Be consistent in defining small, medium, and large benefits across different studies and monographs

NA= not applicable.

Column 8: Absolute Risk Reduction

This describes the difference between the percent of people in the control/placebo group experiencing a specific outcome (control event rate), and the percent of people in the experimental/therapy group experiencing that same outcome (experimental event rate). Mathematically, Absolute risk reduction (ARR) equals experimental event rate minus control event rate.

Many studies do not include adequate data to calculate the ARR, in which cases "NA" is entered into this column.

Column 9: Number Needed to Treat

This is the number of patients who would need to use the therapy under investigation, for the period of time described in the study, in order for one person to experience the specified benefit. It is calculated by dividing the Absolute Risk Reduction into 1 (1/ARR).

Column 10: Comments

When appropriate, this brief section may comment on design flaws (inadequately described subjects, lack of blinding, brief follow-up, not intention-to treat, etc.), notable study design elements (crossover, etc.), dosing, and/or specifics of study group/sub-groups (age, gender, etc).

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