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What is a systematic review, sr workflow visualization, want to learn more.
A systematic review is a comprehensive review of the literature conducted by a research team using systematic and transparent methods in accordance with reporting guidelines to answer a well-defined research question. It aims to identify and synthesize scholarly research published in commercial and/or academic sources as well as in grey (or gray) literature produced by individuals or organizations in order to reduce bias and provide all available evidence for informing practice and policy-making. Systematic reviews may also include a meta-analysis, a more quantitative process of synthesizing and visualizing data retrieved from various studies.
Tsafnat, G., Glasziou, P., Choong, M.K. et al. Systematic review automation technologies . Syst Rev 3, 74 (2014). https://doi.org/10.1186/2046-4053-3-74
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A systematic review is a type of study that synthesises research that has been conducted on a particular topic. Systematic reviews are considered to provide the highest level of evidence on the hierarchy of evidence pyramid. Systematic reviews are conducted following rigorous research methodology. To minimise bias, systematic reviews utilise a predefined search strategy to identify and appraise all available published literature on a specific topic. The meticulous nature of the systematic review research methodology differentiates a systematic review from a narrative review (literature review or authoritative review). This paper provides a brief step by step summary of how to conduct a systematic review, which may be of interest for clinicians and researchers.
Keywords: research; research design; systematic review.
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A systematic review attempts to collate all empirical evidence that fits pre-specified eligibility criteria in order to answer a specific research question. It uses explicit, systematic methods that are selected with a view to minimizing bias, thus providing more reliable findings from which conclusions can be drawn and decisions made (Antman 1992, Oxman 1993) . The key characteristics of a systematic review are:
a clearly stated set of objectives with pre-defined eligibility criteria for studies;
an explicit, reproducible methodology;
a systematic search that attempts to identify all studies that would meet the eligibility criteria;
an assessment of the validity of the findings of the included studies, for example through the assessment of risk of bias; and
a systematic presentation, and synthesis, of the characteristics and findings of the included studies.
Many systematic reviews contain meta-analyses. Meta-analysis is the use of statistical methods to summarize the results of independent studies (Glass 1976). By combining information from all relevant studies, meta-analyses can provide more precise estimates of the effects of health care than those derived from the individual studies included within a review (see Chapter 9, Section 9.1.3 ). They also facilitate investigations of the consistency of evidence across studies, and the exploration of differences across studies.
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Describes what is involved with conducting a systematic review of the literature for evidence-based public health and how the librarian is a partner in the process.
Several CDC librarians have special training in conducting literature searches for systematic reviews. Literature searches for systematic reviews can take a few weeks to several months from planning to delivery.
Fill out a search request form here or contact the Stephen B. Thacker CDC Library by email [email protected] or telephone 404-639-1717.
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Look for systematic reviews that have already been published.
Look in PROSPERO for registered systematic reviews.
Search Cochrane and CRD-York for systematic reviews.
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A systematic review attempts to collect and analyze all evidence that answers a specific question. The question must be clearly defined and have inclusion and exclusion criteria. A broad and thorough search of the literature is performed and a critical analysis of the search results is reported and ultimately provides a current evidence-based answer to the specific question.
Time: According to Cochrane , it takes 18 months on average to complete a Systematic Review.
The average systematic review from beginning to end requires 18 months of work. “…to find out about a healthcare intervention it is worth searching research literature thoroughly to see if the answer is already known. This may require considerable work over many months…” ( Cochrane Collaboration )
Review Team: Team Members at minimum…
“Expert searchers are an important part of the systematic review team, crucial throughout the review process-from the development of the proposal and research question to publication.” ( McGowan & Sampson, 2005 )
*Ask your librarian to write a methods section regarding the search methods and to give them co-authorship. You may also want to consider providing a copy of one or all of the search strategies used in an appendix.
The Question to Be Answered: A clearly defined and specific question or questions with inclusion and exclusion criteria.
Written Protocol: Outline the study method, rationale, key questions, inclusion and exclusion criteria, literature searches, data abstraction and data management, analysis of quality of the individual studies, synthesis of data, and grading of the evidience for each key question.
Literature Searches: Search for any systematic reviews that may already answer the key question(s). Next, choose appropriate databases and conduct very broad, comprehensive searches. Search strategies must be documented so that they can be duplicated. The librarian is integral to this step of the process. Before your librarian creates a search strategy and starts searching in earnest you should write a detailed PICO question , determine the inclusion and exclusion criteria for your study, run a preliminary search, and have 2-4 articles that already fit the criteria for your review.
What is searched depends on the topic of the review but should include…
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Getting Articles:
Articles can be obtained using DocExpress or by searching the electronic journals at the Stephen B. Thacker CDC Library.
IOM Standards for Systematic Reviews: Standard 3.1: Conduct a comprehensive systematic search for evidence
The goal of a systematic review search is to maximize recall and precision while keeping results manageable. Recall (sensitivity) is defined as the number of relevant reports identified divided by the total number of relevant reports in existence. Precision (specificity) is defined as the number of relevant reports identified divided by the total number of reports identified.
Issues to consider when creating a systematic review search:
A step-by-step guide to systematically identify all relevant animal studies
Materials listed in these guides are selected to provide awareness of quality public health literature and resources. A material’s inclusion does not necessarily represent the views of the U.S. Department of Health and Human Services (HHS), the Public Health Service (PHS), or the Centers for Disease Control and Prevention (CDC), nor does it imply endorsement of the material’s methods or findings. HHS, PHS, and CDC assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by HHS, PHS, and CDC. Opinion, findings, and conclusions expressed by the original authors of items included in these materials, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of HHS, PHS, or CDC. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by HHS, PHS, or CDC.
A "high-level overview of primary research on a focused question" utilizing high-quality research evidence through: Source: Kysh, Lynn (2013): Difference between a systematic review and a literature review. [figshare]. Available at:
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Depending on your learning style, please explore the resources in various formats on the tabs above.
For additional tutorials, visit the SR Workshop Videos from UNC at Chapel Hill outlining each stage of the systematic review process.
Know the difference! Systematic review vs. literature review
It is common to confuse systematic and literature reviews as both are used to provide a summary of the existent literature or research on a specific topic. Even with this common ground, both types vary significantly. Please review the following chart (and its corresponding poster linked below) for a detailed explanation of each as well as the differences between each type of review. Source: Kysh, L. (2013). What’s in a name? The difference between a systematic review and a literature review and why it matters. [Poster] Retrieved from . Check the website from UNC at Chapel Hill, |
Types of literature reviews along with associated methodologies
JBI Manual for Evidence Synthesis . Find definitions and methodological guidance.
- Systematic Reviews - Chapters 1-7
- Mixed Methods Systematic Reviews - Chapter 8
- Diagnostic Test Accuracy Systematic Reviews - Chapter 9
- Umbrella Reviews - Chapter 10
- Scoping Reviews - Chapter 11
- Systematic Reviews of Measurement Properties - Chapter 12
Systematic reviews vs scoping reviews -
Grant, M. J., & Booth, A. (2009). A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information and Libraries Journal , 26 (2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
Gough, D., Thomas, J., & Oliver, S. (2012). Clarifying differences between review designs and methods. Systematic Reviews, 1 (28). htt p s://doi.org/ 10.1186/2046-4053-1-28
Munn, Z., Peters, M., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review ? Guidance for authors when choosing between a systematic or scoping review approach. BMC medical research methodology, 18 (1), 143. https://doi.org/10.1186/s12874-018-0611-x. Also, check out the Libguide from Weill Cornell Medicine for the differences between a systematic review and a scoping review and when to embark on either one of them.
Sutton, A., Clowes, M., Preston, L., & Booth, A. (2019). Meeting the review family: Exploring review types and associated information retrieval requirements . Health Information & Libraries Journal , 36 (3), 202–222. https://doi.org/10.1111/hir.12276
Temple University. Review Types . - This guide provides useful descriptions of some of the types of reviews listed in the above article.
UMD Health Sciences and Human Services Library. Review Types . - Guide describing Literature Reviews, Scoping Reviews, and Rapid Reviews.
Whittemore, R., Chao, A., Jang, M., Minges, K. E., & Park, C. (2014). Methods for knowledge synthesis: An overview. Heart & Lung: The Journal of Acute and Critical Care, 43 (5), 453–461. https://doi.org/10.1016/j.hrtlng.2014.05.014
Differences between a systematic review and other types of reviews
Armstrong, R., Hall, B. J., Doyle, J., & Waters, E. (2011). ‘ Scoping the scope ’ of a cochrane review. Journal of Public Health , 33 (1), 147–150. https://doi.org/10.1093/pubmed/fdr015
Kowalczyk, N., & Truluck, C. (2013). Literature reviews and systematic reviews: What is the difference? Radiologic Technology , 85 (2), 219–222.
White, H., Albers, B., Gaarder, M., Kornør, H., Littell, J., Marshall, Z., Matthew, C., Pigott, T., Snilstveit, B., Waddington, H., & Welch, V. (2020). Guidance for producing a Campbell evidence and gap map . Campbell Systematic Reviews, 16 (4), e1125. https://doi.org/10.1002/cl2.1125. Check also this comparison between evidence and gaps maps and systematic reviews.
Rapid Reviews Tutorials
Rapid Review Guidebook by the National Collaborating Centre of Methods and Tools (NCCMT)
Hamel, C., Michaud, A., Thuku, M., Skidmore, B., Stevens, A., Nussbaumer-Streit, B., & Garritty, C. (2021). Defining Rapid Reviews: a systematic scoping review and thematic analysis of definitions and defining characteristics of rapid reviews. Journal of clinical epidemiology , 129 , 74–85. https://doi.org/10.1016/j.jclinepi.2020.09.041
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Videos on systematic reviews
This video lecture explains in detail the steps necessary to conduct a systematic review (44 min.) | Here's a brief introduction to how to evaluate systematic reviews (16 min.) |
Systematic Reviews: What are they? Are they right for my research? - 47 min. video recording with a closed caption option.
More training videos on systematic reviews:
from Yale University (approximately 5-10 minutes each) | with Margaret Foster (approximately 55 min each) |
Books on Systematic Reviews
Books on Meta-analysis
Guidelines for a systematic review as part of the dissertation
Further readings on experiences of PhD students and doctoral programs with systematic reviews
Puljak, L., & Sapunar, D. (2017). Acceptance of a systematic review as a thesis: Survey of biomedical doctoral programs in Europe . Systematic Reviews , 6 (1), 253. https://doi.org/10.1186/s13643-017-0653-x
Perry, A., & Hammond, N. (2002). Systematic reviews: The experiences of a PhD Student . Psychology Learning & Teaching , 2 (1), 32–35. https://doi.org/10.2304/plat.2002.2.1.32
Daigneault, P.-M., Jacob, S., & Ouimet, M. (2014). Using systematic review methods within a Ph.D. dissertation in political science: Challenges and lessons learned from practice . International Journal of Social Research Methodology , 17 (3), 267–283. https://doi.org/10.1080/13645579.2012.730704
UMD Doctor of Philosophy Degree Policies
Before you embark on a systematic review research project, check the UMD PhD Policies to make sure you are on the right path. Systematic reviews require a team of at least two reviewers and an information specialist or a librarian. Discuss with your advisor the authorship roles of the involved team members. Keep in mind that the UMD Doctor of Philosophy Degree Policies (scroll down to the section, Inclusion of one's own previously published materials in a dissertation ) outline such cases, specifically the following:
" It is recognized that a graduate student may co-author work with faculty members and colleagues that should be included in a dissertation . In such an event, a letter should be sent to the Dean of the Graduate School certifying that the student's examining committee has determined that the student made a substantial contribution to that work. This letter should also note that the inclusion of the work has the approval of the dissertation advisor and the program chair or Graduate Director. The letter should be included with the dissertation at the time of submission. The format of such inclusions must conform to the standard dissertation format. A foreword to the dissertation, as approved by the Dissertation Committee, must state that the student made substantial contributions to the relevant aspects of the jointly authored work included in the dissertation."
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Collaboration for Environmental Evidence. 2018. Guidelines and Standards for Evidence synthesis in Environmental Management. Version 5.0 (AS Pullin, GK Frampton, B Livoreil & G Petrokofsky, Eds) www.environmentalevidence.org/information-for-authors .
Pullin, A. S., & Stewart, G. B. (2006). Guidelines for systematic review in conservation and environmental management. Conservation Biology, 20 (6), 1647–1656. https://doi.org/10.1111/j.1523-1739.2006.00485.x
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A systematic review is an evidence synthesis that uses explicit, reproducible methods to perform a comprehensive literature search and critical appraisal of individual studies and that uses appropriate statistical techniques to combine these valid studies.
Generally, systematic reviews must have:
A meta-analysis is a systematic review that uses quantitative methods to synthesize and summarize the pooled data from included studies.
https://doi.org/10.1136/ebn.2011.0049
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A high-quality systematic review is described as the most reliable source of evidence to guide clinical practice. The purpose of a systematic review is to deliver a meticulous summary of all the available primary research in response to a research question. A systematic review uses all the existing research and is sometime called ‘secondary research’ (research on research). They are often required by research funders to establish the state of existing knowledge and are frequently used in guideline development. Systematic review findings are often used within the …
Competing interests None.
Assessing the certainty of the evidence in systematic reviews: importance, process, and use.
Romina Brignardello-Petersen, Gordon H Guyatt, Assessing the Certainty of the Evidence in Systematic Reviews: Importance, Process, and Use, American Journal of Epidemiology , 2024;, kwae332, https://doi.org/10.1093/aje/kwae332
When interpreting results and drawing conclusions, authors of systematic reviews should consider the limitations of the evidence included in their review. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach provides a framework for the explicit consideration of the limitations of the evidence included in a systematic review, and for incorporating this assessment into the conclusions. Assessments of certainty of evidence are a methodological expectation of systematic reviews. The certainty of the evidence is specific to each outcome in a systematic review, and can be rated as high, moderate, low, or very low. Because it will have an important impact, before conducting certainty of evidence, reviewers must clarify the intent of their question: are they interested in causation or association. Serious concerns regarding limitations in the study design, inconsistency, imprecision, indirectness, and publication bias can decrease the certainty of the evidence. Using an example, this article describes and illustrates the importance and the steps for assessing the certainty of evidence and drawing accurate conclusions in a systematic review.
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MA indicates meta-analysis.
ROR indicates ratio of odds ratio.
eAppendix 1. Search Strategy
eAppendix 2. Data Extraction
eTable 1. The Composite Primary Outcome and Effect Estimates of Mega-Trials Identified by Our Search but Analyzed Only for a Subset of the Primary Outcome
eAppendix 3. Mega-Trials Not Included in Meta-Analyses
eTable 4. Characteristics of Mega-Trials Identified by Our Search but Had No Eligible Meta-Analysis
eTable 2. Characteristics of the Additional Identified Mega-Trials That Have Not Been Identified by Our Search
eAppendix 4. Meta-Analyses of Mega-Trials vs Smaller Trials for the Primary Outcome
eFigure 1. Agreement Between Mega-Trials and Smaller Trials for Primary Outcome: Random Effects (DerSimonian Laird)
eAppendix 5. Meta-Analyses of Mega-Trials vs Smaller Trials for All-Cause Mortality
eFigure 2. Agreement Between Mega-Trials and Smaller Trials for All-Cause Mortality: Random Effects (DerSimonian Laird)
eFigure 3. Agreement Between Smaller Trials Prior and After the Publication of the First Mega-Trial
eTable 3. Results of Uni- and Multivariable Meta-Regression
eFigure 4. Agreement Between Mega-Trials and Smaller Trials With 1/5 of the Least Weighted Mega-Trial
eFigure 5. Agreement Between Mega-Trials and Smaller Trials With 1/10 of the Least Weighted Megatrial
eFigure 6. Agreement Between Mega-Trials and Smaller Trials, Pooling the Results Using Fixed Effects
eFigure 7. Agreement Between Mega-Trials and Smaller Trials, Pooling the Results Using Random Effects – HKSJ Method
eFigure 8. Agreement Between Mega-Trials and Smaller Trials Stratified to Blinding
eFigure 9. Agreement Between Mega-Trials and Smaller Trials Stratified to Intervention Type
eFigure 10. Agreement Between Mega-Trials and Smaller Trials Stratified to Specialty
eFigure 11. Agreement Between Mega-Trials and Smaller Trials Stratified to Heterogeneity
eFigure 12. Agreement Between Trials With More Than 30,000 Participants and Smaller Trial for the Primary Outcome
eFigure 13. Agreement Between Mega-Trials When More Than One Was Present in a Meta-Analysis–Primary Outcome
eReferences.
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Kastrati L , Raeisi-Dehkordi H , Llanaj E, et al. Agreement Between Mega-Trials and Smaller Trials : A Systematic Review and Meta-Research Analysis . JAMA Netw Open. 2024;7(9):e2432296. doi:10.1001/jamanetworkopen.2024.32296
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Question Are the results of mega-trials with 10 000 participants or more similar to meta-analysis of trials with smaller sample sizes for the primary outcome and/or all-cause mortality?
Findings In this meta-research analysis of 82 mega-trials, meta-analyses of smaller studies showed overall comparable results with mega-trials, but smaller trials published before the mega-trials gave more favorable results than mega-trials. There were very low rates of significant results for the primary outcome and all-cause mortality for mega-trials.
Meaning The findings of this study suggest that mega-trials need to be performed more often, given the relative low number of mega-trials found, their low significant rates, and the fact that smaller trials published prior to mega-trial reported more beneficial results than mega-trials and subsequent smaller trials.
Importance Mega-trials can provide large-scale evidence on important questions.
Objective To explore how the results of mega-trials compare with the meta-analysis results of trials with smaller sample sizes.
Data Sources ClinicalTrials.gov was searched for mega-trials until January 2023. PubMed was searched until June 2023 for meta-analyses incorporating the results of the eligible mega-trials.
Study Selection Mega-trials were eligible if they were noncluster nonvaccine randomized clinical trials, had a sample size over 10 000, and had a peer-reviewed meta-analysis publication presenting results for the primary outcome of the mega-trials and/or all-cause mortality.
Data Extraction and Synthesis For each selected meta-analysis, we extracted results of smaller trials and mega-trials included in the summary effect estimate and combined them separately using random effects. These estimates were used to calculate the ratio of odds ratios (ROR) between mega-trials and smaller trials in each meta-analysis. Next, the RORs were combined using random effects. Risk of bias was extracted for each trial included in our analyses (or when not available, assessed only for mega-trials). Data analysis was conducted from January to June 2024.
Main Outcomes and Measures The main outcomes were the summary ROR for the primary outcome and all-cause mortality between mega-trials and smaller trials. Sensitivity analyses were performed with respect to the year of publication, masking, weight, type of intervention, and specialty.
Results Of 120 mega-trials identified, 41 showed a significant result for the primary outcome and 22 showed a significant result for all-cause mortality. In 35 comparisons of primary outcomes (including 85 point estimates from 69 unique mega-trials and 272 point estimates from smaller trials) and 26 comparisons of all-cause mortality (including 70 point estimates from 65 unique mega-trials and 267 point estimates from smaller trials), no difference existed between the outcomes of the mega-trials and smaller trials for primary outcome (ROR, 1.00; 95% CI, 0.97-1.04) nor for all-cause mortality (ROR, 1.00; 95% CI, 0.97-1.04). For the primary outcomes, smaller trials published before the mega-trials had more favorable results than the mega-trials (ROR, 1.05; 95% CI, 1.01-1.10) and subsequent smaller trials published after the mega-trials (ROR, 1.10; 95% CI, 1.04-1.18).
Conclusions and Relevance In this meta-research analysis, meta-analyses of smaller studies showed overall comparable results with mega-trials, but smaller trials published before the mega-trials gave more favorable results than mega-trials. These findings suggest that mega-trials need to be performed more often given the relative low number of mega-trials found, their low significant rates, and the fact that smaller trials published prior to mega-trial report more beneficial results than mega-trials and subsequent smaller trials.
Most randomized comparisons of interventions in medicine use small to modest sample sizes. The call for more mega-trials (ie, large sample trials) with over 10 000 participants has been longstanding. 1 , 2 Mega-trials have been rare, but there has been a renewed interest recently. Several mega-trials have found that certain interventions, like vitamin D supplementation, may not be as effective as previously thought. 3 , 4 Conversely, other mega-trials, such as the Second International Study of Infarct Survival (ISIS-2) Collaborative Group trial on streptokinase and aspirin after myocardial infarction 5 found favorable results with major clinical impact. Conducting mega-trials may be facilitated by the growth of interest in pragmatic (ie, practical) research, 6 , 7 new platforms for recruitment of participants, 8 and wider recognition of the limitations of small trials. Therefore, it is important to understand and compare the results of mega-trials with those of smaller trials.
Meta-analyses rarely include large trials, and small trials have traditionally been considered more susceptible to biases, including more prominent selective reporting. 9 , 10 Previous literature comparing results of meta-analyses of small trials with subsequent large trials has shown heterogeneous results. 11 - 16 Furthermore, different methods have been proposed to analyze the agreement. 17 Different event rates in the control group of the considered trials (baseline risk), differences in trial quality, and variable susceptibility to bias of the health outcomes under investigation may also generate heterogeneity. 11 Moreover, mega-trials and smaller trials may have topic- and question-specific biases that are different in the 2 groups. In previous work, there was also no clear consensus on what constitutes a large trial. Some 18 have considered the amount of evidence in each trial (inverse of variance or sample size) as a continuum, while others tried to separate trials with sufficient power (eg, 80%) to detect plausible effects, 19 and yet others used arbitrary sample size thresholds, (eg, 1000 participants). 12 , 14 To our knowledge, no comprehensive empirical examination has systematically compared the results of mega-trials with sample sizes exceeding 10 000 participants versus smaller trials.
Here, we aimed to systematically identify such mega-trials, identify which ones have been included in meta-analyses for their primary outcomes and/or for mortality outcomes, compare the results of these mega-trials against the combined results of smaller trials, and identify potential factors associated with discrepancies.
This meta-analysis was a meta-research project; because this study is not a typical meta-analysis, we followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses ( PRISMA ) reporting guideline where applicable. 18 The original protocol was registered in Open Science Framework Because the information we used consisted of publicly available results of RCTs, and not patient-specific data, there was no need for ethical review. We analyzed meta-analyses of clinical trials that have included mega-trial results in their analysis for calculations of a summary effect size for the primary end point of the mega-trial. Additionally, we considered data on all-cause mortality as a secondary outcome because it is the most severe and objective outcome.
Mega-trials were considered for analysis if they were noncluster, nonvaccine randomized clinical trials (RCTs) regardless of masking; had a sample size of more than 10 000 participants; had a peer-reviewed publication presenting the results of the primary end point; and were included in a meta-analysis for their primary outcome and/or all-cause mortality. We excluded cluster trials because the effective sample size is much smaller than the number of participants. We excluded vaccine trials because very large vaccine trials usually have different considerations and types of outcomes than mega-trials of other interventions.
For a meta-analysis to be included in the analysis, it had to have a systematic review design and include the results of the mega-trial along with any number of other trials in obtaining summary effect size estimates with the effect size and variance data available (or possible to calculate) for each trial from presented information.
We searched for mega-trials in ClinicalTrials.gov (last updated January 2023) and then performed PubMed searches (until June 2023) to identify the most recent meta-analyses that included the results of these mega-trials for the primary outcome of the mega-trial and for all-cause mortality. Details on the search process are in eAppendix 1 in Supplement 1 .
For each selected meta-analysis, we extracted the results of RCTs included in the summary effect size estimate that incorporated the effect size estimate of the mega-trial. We also extracted information, whenever available, on the risk of bias assessments for each included trial based on Cochrane Risk of Bias Tools (original, revised, and version 2). All data extractions (except mega-trial identification) were performed by 2 reviewers (L.K. and H.R.D.; L.K. and H.G.Q.P.; L.K. and E.L.L.; L.K. and N.S.A.; L.K. and F.K.; L.K. and R.M.; and L.K. and A.L.L.), and differences were settled by discussion. For any unsettled discrepancies, a third senior reviewer (T.M.) was invited to arbitrate. Details on data extraction appear in eAppendix 2 in Supplement 1 .
Some of the eligible meta-analyses contained results from other mega-trials that had not been detected by our search. Therefore, we described these extra identified trials and included them in our analyses. We extracted information for all mega-trials based on whether they found statistically significant or nonsignificant results and whether they were designed to show noninferiority. In several meta-analyses, some trials did not pass the 10 000-participant threshold but were substantially large enough to blur the effects. Therefore, in a sensitivity analysis, we compared the results of mega-trials vs only the smaller trials that weighted less than one-fifth of the least weighted mega-trial; in another sensitivity analysis, we compared the results of mega-trials vs smaller trials that weighted less than one-tenth of the least weighted mega-trial. We then further restricted these trials to those published only before or up to the first trial. We also explored the agreement on different thresholds, setting the threshold at a sample size of 30 000. In addition, we also compared the agreement between the mega-trials, when more than one was included in a meta-analysis. Finally, we also assessed the risk of bias for the mega-trials that had not been assessed (or had been assessed using various non-Cochrane tools [eg, Jadad scale]) using the Cochrane Risk-of-Bias Tool. 25
In each eligible meta-analysis, we combined the results from non–mega-trials using random effects (and fixed effects as sensitivity analysis) and compared them against the results of the mega-trial. In meta-analyses where several mega-trials were available, the results of the mega trials were combined using random effects first before being compared against the results of smaller trials. Any cluster trials were considered to be non–mega-trials. 20
The odds ratio (OR) was the metric of choice. All the analyzed outcomes were dichotomous. Between-trial heterogeneity assessments used τ 2 between-study variance estimator, Q test, and I 2 statistics. 21
We obtained the log ratio of ORs (ROR) and its variance (the sum of the variances of the logOR in the 2 groups) between the mega-trials and the smaller trials for each eligible outcome. Then, the logROR estimates were combined across each outcome using the DerSimonian-Laird random-effects calculations. 22 We also performed sensitivity analyses using the Hartung-Knapp-Sidik-Jonkman (HKSJ) method. 23 In all calculations, treatment effects in single trials and meta-analyses thereof were coined consistently so that an ROR less than 1 means a more favorable outcome for the intervention group over the control group.
A sensitivity analysis was performed to assess whether the results were different when non–mega-trials were included in the calculations only if they were published up until (and including) the year of publication of any mega-trials and comparing them with the results of the mega-trial. This analysis more specifically targets the research question of whether mega-trials corroborate the results of smaller trials that have been performed before them. A separate analysis also compared the results of non–mega-trials published up until the year of publication of the mega-trial vs non–mega-trials published subsequently.
Separate subgroup analyses were performed for the comparison of results in mega-trials vs other trials according to masking (open-label vs masked), intervention type, specialty (eg, cardiovascular), and per heterogeneity (low vs non-low) of the mega-trials. We also performed exploratory meta-regressions considering the same variables (masking, type of outcome, type of intervention, and specialty) and also risk of bias in the mega-trials (high vs other), risk of bias in the other trials (proportion at high risk), median number of participants in non–mega-trials, and total number of participants in non–mega-trials. We also performed exploratory tests for small study effect sizes (Egger test), 24 when there were more than 10 trials.
Analyses were conducted using Stata software version 17 (StataCorp). The threshold for significance was a 2-tailed P < .05. Data analysis occurred from January to June 2024.
A total of 180 registered completed phase 3 or 4 mega-trials that did not involve vaccines and that had 10 000 or more participants were identified through our search ( Figure 1 ). Among these, 91 were randomized, noncluster, nonvaccine mega-trials; but 35 of these 91 trials lacked an appropriate meta-analysis and 2 had no published results, leaving 51 mega-trials with an eligible meta-analysis for either primary outcome and/or all-cause mortality. Three trials registered with more than 10 000 participants and had eligible meta-analyses; however, they randomized less 10 000 participants and were excluded by our analyses. 26 - 28 Results were compared to smaller trials across 58 meta-analyses, including 35 for primary outcome 29 - 75 , 152 and 26 for all-cause mortality. 29 , 32 - 35 , 37 - 47 , 49 - 54 , 56 - 62 , 64 - 70 , 72 - 74 , 76 - 78 In 3 studies, 32 , 41 , 68 all-cause mortality was the mega-trial’s primary outcome ( Table 1 ). For 19 mega-trials that had a composite primary outcome , 30 , 32 , 33 , 39 , 42 , 45 , 46 , 48 , 53 , 55 , 56 , 59 , 61 , 62 , 66 , 68 , 69 , 71 , 152 no eligible meta-analysis was identified for the complete composite outcome, therefore the meta-analysis of one of the subsets of the composite outcome with the highest number of events was analyzed ( Table 1 and eTable 1, eAppendix 3, and eTable 4 in Supplement 1 ).
The eligible meta-analyses included estimates from another 30 mega-trials 79 - 108 that had randomized, noncluster design and more than 10 000 participants but had not been identified in our searches (eTable 2 in Supplement 1 ). Of these 30 studies, 26 were not registered in ClinicalTrials.gov, 79 - 84 , 86 - 94 , 96 - 101 , 103 - 107 while 2 85 , 108 had no listed location in ClinicalTrials.gov, 1 95 had listed no results in ClinicalTrials.gov, and for 1 study, 102 no reason for missingness was identified. These 30 trials with their estimates for primary outcomes (20 trials) and all-cause mortality (22 trials) were considered in the mega-trials group in all calculations. The meta-analyses included an additional 1 mega-trial that had initially been identified by our search but had no eligible meta-analysis for the primary outcome and/or all-cause mortality but was meta-analyzed for another outcome. 109 In total, 82 mega-trials were included across all meta-analyses for the primary outcome (69 mega-trials 29 - 75 , 79 , 80 , 84 - 87 , 89 - 94 , 97 - 100 , 102 - 104 , 108 , 109 , 152 ) and all-cause mortality (65 mega-trials 29 , 32 - 35 , 37 - 47 , 49 - 54 , 56 - 62 , 64 - 67 , 69 , 70 , 72 - 74 , 76 - 83 , 85 , 87 - 89 , 92 - 96 , 99 , 101 - 107 , 109 , 152 ).
Of the 82 mega-trials 29 - 109 , 152 included in our analyses, 64 30 , 31 , 33 - 40 , 42 - 74 , 76 - 86 , 89 - 94 , 96 - 98 , 100 , 102 - 106 , 108 , 109 investigated cardiovascular outcomes, 17 mega-trials 31 , 38 , 49 , 57 , 65 , 73 , 80 , 88 , 93 , 95 , 97 , 98 , 100 , 101 , 107 - 109 were centered around nutritional interventions, and 1 mega-trial 75 covered various other medical interventions intervention types, such as pharmacological treatment ( Table 1 and eTable 1 and eTable 2 in Supplement 1 ). Moreover, 15 of the mega-trials were open-label, 29 , 37 , 47 , 57 , 68 , 73 , 79 - 81 , 86 , 87 , 90 , 102 , 105 , 106 while the remaining 65 mega-trials were double-blinded, and 2 trials employed varying degrees of masking ( Table 1 ). Of all the mega-trials, 14 29 , 44 , 47 , 52 , 68 , 72 , 73 , 79 , 81 , 87 , 97 , 102 , 106 , 152 were judged at high risk of bias. A total of 32 mega-trials 29 , 30 , 35 , 37 , 39 , 40 , 43 , 45 , 51 , 54 , 55 , 58 , 60 , 64 , 69 , 71 , 73 , 76 , 78 - 80 , 82 , 85 , 87 , 90 , 92 , 96 , 101 , 102 , 105 , 106 had statistically significant results at P < .05 for the primary outcome (30 favoring the intervention group) and only 17 29 , 33 , 43 , 47 , 48 , 50 , 58 , 61 , 69 , 76 , 79 , 80 , 82 , 86 , 99 , 101 , 106 had statistically significant results at P < .05 for all-cause mortality (13 favoring the intervention group) ( Table 1 and eTable 1 and eTable 2 in Supplement 1 ).
A total of 35 comparisons of mega-trials vs other trials were available, 110 - 138 yielding a total of 85 point estimates coming from 69 unique mega-trials. 29 - 62 , 64 - 106 , 109 , 152 These 69 mega-trials yielded a median (IQR) of 15 715 (12 530-20 114) participants ( Table 2 ). The total number of smaller trials across these 35 mega-trials was 272 (median [range], 6 [1-45] smaller trials) ( Table 2 ). There was a median (IQR) of 1639 (297-4128) participants across the 35 studies from the smaller trials. Of the 272 smaller trials, 133 were published before or up to the year of the first mega-trial of the respective topic. In 7 meta-analyses, 110 , 114 , 117 , 121 , 124 , 132 , 137 the cumulative sample size of all the other smaller trials exceeded the cumulative sample size of the mega-trials ( Table 2 ).
Detailed information with forest plots on all of the 35 meta-analyses 110 - 138 appears in eAppendix 4 in Supplement 1 . In the summary analysis, there was no noteworthy discrepancy observed between the results of the mega-trials and those of smaller trials (summary ROR, 1.00; 95% CI, 0.97-1.04; I 2 = 0.0; P for heterogeneity = .48) (eFigure 1 in Supplement 1 ). There were 2 instances when disagreement between the mega-trials and the respective smaller trials was beyond chance; the first 112 was comparing ivabradine with placebo for major adverse cardiovascular event (ROR, 1.21; 95% CI, 1.00-1.47), and the second 126 was a comparison of new adenosine diphosphate receptor agonist with clopidogrel for myocardial infarction (ROR, 0.83; 95% CI, 0.73-0.95). ,
A total of 26 comparisons of mega-trials vs other trials were available. 112 - 115 , 118 , 119 , 122 , 127 , 128 , 130 , 133 , 134 , 136 , 138 - 145 and 70 estimates coming from 65 unique mega-trials 29 , 32 - 35 , 37 - 47 , 49 - 54 , 56 - 62 , 64 - 67 , 69 , 70 , 72 - 74 , 76 - 83 , 85 , 87 - 89 , 92 - 96 , 99 , 101 - 107 , 109 , 152 were considered in these comparisons ( Table 3 ). The median (IQR) total number of participants in all of the mega-trials was 15 919 (12 524-18 857).
The total number of smaller trials in these 26 meta-analyses was 268 (median [range] per meta-analysis, 6 [1-47] smaller trials). There was a median (IQR) of 1132 (250-4038) participants from smaller trials. Of the 268 smaller trials, 117 were published before or up to the year of the first mega-trial of the respective topic. In 5 meta-analyses, 132 , 139 - 141 , 144 the cumulative number of participants in the other smaller trials exceeded the total number of participants in the mega-trials ( Table 3 ). Comprehensive details and forest plots about the 26 meta-analyses appear in eAppendix 5 in Supplement 1 .
In the summary analysis, no difference existed between the outcomes of the mega-trials and those of the smaller trials (summary ROR, 1.00; 95% CI, 0.97-1.04; I 2 = 0.0%; P for heterogeneity = .60) (eFigure 2 in Supplement 1 ). In one instance testing effects of anti-inflammatory vs placebo in patients with coronary artery diseases, 128 the results differed beyond chance between mega-trials and the other smaller trials (ROR, 0.79; 95% CI, 0.65-0.97), with mega-trial showing no effect but meta-analysis of smaller trials showing an increased risk.
Smaller trials showed significantly larger effects for the primary outcome when compared with mega-trials when they were published before the first megatrial (ROR, 1.05; 95% CI, 1.01-1.10), and similar direction but nonsignificant effect for all-cause mortality (ROR, 1.03; 95% CI, 0.98-1.09) ( Figure 2 , A and B). Results of smaller trials published before the mega-trial showed significantly higher benefits as compared with smaller trials published subsequently for primary outcome (ROR, 1.10; 95% CI, 1.04-1.18) and similar outcomes for all-cause mortality (ROR, 1.06; 95% CI, 0.98-1.15) (eFigure 3 in Supplement 1 ).
No difference was seen when results were pooled using fixed effects, having a threshold of 30 000 participants using HKSJ random effects. Other subgroup analyses and meta-regressions were also nonrevealing (eTable 3 and eFigures 4-13 in Supplement 1 ). No small-study effects were found for the meta-analyses for the primary outcome and 1 meta-analysis 140 had a significant small-study effects result for all-cause mortality.
In total, we analyzed and/or described the results from 120 mega-trials. Of the 120, 41 showed a significant result for the primary outcome (33 of which favored intervention over control) and 22 showed a significant result for all-cause mortality ( and 18 of which favored intervention over control). For the 17 studies with noninferiority designs, 15 had reached noninferiority and 2 had significantly better results in the experimental group vs the control group for the primary outcome ( Table 1 and eTable 1 and eTable 2 in Supplement 1 ).
Overall, this meta-analysis of mega-trials found that outcomes from meta-analyses of other smaller clinical trials aligned on average with those of mega-trials in the clinical studies that we examined. This finding could be partly explained by the relatively large sample size of the smaller trials. However, mega-trials tended to have less favorable results than the smaller trials that preceded them timewise, and smaller trials published after the mega-trials tended to have less favorable results than the smaller trials published before the mega-trials and aligned with the mega-trials. Most mega-trials do not show statistically significant benefits for the primary outcome of interest, and statistically significant benefits for mortality are rare. Mega-trials are not available for most medical studies. Given that small trials and their meta-analyses may give unreliable, inflated estimates of benefit, mega-trials, or at least substantially large trials with sufficient power, may need to be considered and performed more frequently.
The diminished benefits in late smaller trials vs early small trials were also consistent with prior meta-research studies 146 that have shown that the reported effects of interventions change over time, with wider oscillations of results in early studies. It has been observed that it is more frequent for treatment effects to decrease rather than increase over time. 147 - 149 In our examined studies, the mega-trials may have corrected some inflated effects seen in the earlier trials that preceded them. Then, the subsequent trials might have been more aligned with what the mega-trials had shown because the mega-trials are likely to have been considered very influential.
Previous meta-research assessments have shown different levels of agreement between the results of meta-analyses of smaller trials and large clinical trials. For example, Cappelleri et al 11 reported compatible results of meta-analysis of smaller studies with the results of large trials, although discrepancies in their results were found in up to 10% of the cases. However, other meta-studies on this topic 13 showed larger differences with a discrepancy rate of up to 39%. These previous studies used a definition of a large trial having enrolled 1000 participants or more. In contrast, we used a sample size of 10 000 participants to define a mega-trial, and therefore had a larger power to detect effects.
This study has limitations. Several early mega-trials are not included in the ClinicalTrials.gov registry. Nevertheless, we were able to identify several of these trials because they were included in the meta-analyses of other mega-trials, and they were considered in our calculations.
Our comparative results vs smaller trials still did not include all mega-trials, because for some mega-trials retrieved in ClinicalTrials.gov, we found no relevant meta-analysis where they had been included. However, we did examine the main conclusions of these mega-trials and they also had low rates of statistically significant results. Therefore, we can conclude that mega-trials in general tend to give negative results for tested interventions.
Mega-trials may have, on average, more pragmatic designs than smaller trials. The different eligibility criteria and different populations of participants enrolled in mega-trials vs smaller trials may create differences in effect sizes. Addressing such differences in case-mix heterogeneity would require individual-level data.
Mega-trials are unlikely to be launched unless there is genuine equipoise. Nevertheless, the low rate of significant benefits, as opposed to the much higher rates of favorable results seen in typical phase 3 trials, is remarkable. 150 Previous research found more favorable results in industry-funded research. 150 , 151 Finally, our analyses depend on the accuracy and quality of data extracted from the included meta-analyses.
In this meta-research analysis, meta-analyses of smaller studies showed, in general, comparable results with mega-trials, but smaller trials published before the mega-trials gave more favorable results than the mega-trials. Mega-trials are done very sparingly to date, but it would be beneficial to add more of these trials to the clinical research armamentarium. 152 , 153
Accepted for Publication: July 12, 2024.
Published: September 6, 2024. doi:10.1001/jamanetworkopen.2024.32296
Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Kastrati L et al. JAMA Network Open .
Corresponding Author: John P. A. Ioannidis, MD, DSc, Meta-Research Innovation Center at Stanford (METRICS), Stanford University, 1265 Welch Rd, M/C 5411, Stanford, CA 94305 ( [email protected] ).
Author Contributions: Drs Kastrati and Ioannidis had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Kastrati, Muka, Ioannidis.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Kastrati, Quezada-Pinedo, Khatami, Ahanchi, Muka, Ioannidis.
Critical review of the manuscript for important intellectual content: Kastrati, Raeisi-Dehkordi, Llanaj, Quezada-Pinedo, Khatami, Llane, Meçani, Muka, Ioannidis.
Statistical analysis: Kastrati, Llanaj, Quezada-Pinedo, Khatami, Llane, Muka, Ioannidis.
Obtained funding: Ahanchi.
Administrative, technical, or material support: Quezada-Pinedo, Ahanchi.
Supervision: Ioannidis.
Conflict of Interest Disclosures: Dr Muka reported receiving grants from Merz Aesthetics; personal fees from Merz Aesthetics; and serving as cofounder and CEO at Epistudia GmbH outside the submitted work. No other disclosures were reported.
Funding/Support: This study was supported by an unrestricted gift from Sue and Bob O’Donnell to Stanford University (to Dr Ioannidis), the Swiss Government (scholarship for excellence to Dr Kastrati), University of Bern, and Insel Spital (funding to Dr Kastrati).
Role of the Funder/Sponsor: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Data Sharing Statement: See Supplement 2 .
Research output : Contribution to journal › Article › Research › peer-review
Background: To examine whether lack of measurement invariance (MI) influences mean comparisons among different disease groups, this paper provides (1) a systematic review of MI in generic constructs across chronic conditions and (2) an empirical analysis of MI in the Health Education Impact Questionnaire (heiQ™).Methods: (1) We searched for studies of MI among different chronic conditions in online databases. (2) Multigroup confirmatory factor analyses were used to study MI among five chronic conditions (orthopedic condition, rheumatism, asthma, COPD, cancer) in the heiQ™ with N = 1404 rehabilitation inpatients. Impact on latent and composite mean differences was examined.Results: (1) A total of 30 relevant studies suggested that about one in three items lacked MI. However, only four studies examined impact on latent mean differences. Scale means were only affected in one of these three studies. (2) Across the eight heiQ™ scales, seven scales had items with lack of MI in at least one disease group. However, in only two heiQ™ scales were some latent or composite mean differences affected.Conclusions: Lack of MI among disease groups is common and may have a relevant influence on mean comparisons when using generic instruments. Therefore, when comparing disease groups, tests of MI should be implemented. More studies of MI and according impact on mean differences in generic questionnaires are needed.
Original language | English |
---|---|
Article number | 56 |
Number of pages | 12 |
Journal | |
Volume | 12 |
Issue number | 1 |
DOIs | |
Publication status | Published - 23 Apr 2014 |
Externally published | Yes |
T1 - Measurement invariance across chronic conditions
T2 - A systematic review and an empirical investigation of the Health Education Impact Questionnaire (heiQ™)
AU - Schuler, Michael
AU - Musekamp, Gunda
AU - Bengel, Jürgen
AU - Nolte, Sandra
AU - Osborne, Richard H.
AU - Faller, Hermann
PY - 2014/4/23
Y1 - 2014/4/23
N2 - Background: To examine whether lack of measurement invariance (MI) influences mean comparisons among different disease groups, this paper provides (1) a systematic review of MI in generic constructs across chronic conditions and (2) an empirical analysis of MI in the Health Education Impact Questionnaire (heiQ™).Methods: (1) We searched for studies of MI among different chronic conditions in online databases. (2) Multigroup confirmatory factor analyses were used to study MI among five chronic conditions (orthopedic condition, rheumatism, asthma, COPD, cancer) in the heiQ™ with N = 1404 rehabilitation inpatients. Impact on latent and composite mean differences was examined.Results: (1) A total of 30 relevant studies suggested that about one in three items lacked MI. However, only four studies examined impact on latent mean differences. Scale means were only affected in one of these three studies. (2) Across the eight heiQ™ scales, seven scales had items with lack of MI in at least one disease group. However, in only two heiQ™ scales were some latent or composite mean differences affected.Conclusions: Lack of MI among disease groups is common and may have a relevant influence on mean comparisons when using generic instruments. Therefore, when comparing disease groups, tests of MI should be implemented. More studies of MI and according impact on mean differences in generic questionnaires are needed.
AB - Background: To examine whether lack of measurement invariance (MI) influences mean comparisons among different disease groups, this paper provides (1) a systematic review of MI in generic constructs across chronic conditions and (2) an empirical analysis of MI in the Health Education Impact Questionnaire (heiQ™).Methods: (1) We searched for studies of MI among different chronic conditions in online databases. (2) Multigroup confirmatory factor analyses were used to study MI among five chronic conditions (orthopedic condition, rheumatism, asthma, COPD, cancer) in the heiQ™ with N = 1404 rehabilitation inpatients. Impact on latent and composite mean differences was examined.Results: (1) A total of 30 relevant studies suggested that about one in three items lacked MI. However, only four studies examined impact on latent mean differences. Scale means were only affected in one of these three studies. (2) Across the eight heiQ™ scales, seven scales had items with lack of MI in at least one disease group. However, in only two heiQ™ scales were some latent or composite mean differences affected.Conclusions: Lack of MI among disease groups is common and may have a relevant influence on mean comparisons when using generic instruments. Therefore, when comparing disease groups, tests of MI should be implemented. More studies of MI and according impact on mean differences in generic questionnaires are needed.
KW - Chronic disease
KW - Generic questionnaire
KW - Measurement invariance
KW - Systematic review
UR - http://www.scopus.com/inward/record.url?scp=84900297807&partnerID=8YFLogxK
U2 - 10.1186/1477-7525-12-56
DO - 10.1186/1477-7525-12-56
M3 - Article
C2 - 24758346
AN - SCOPUS:84900297807
SN - 1477-7525
JO - Health and Quality of Life Outcomes
JF - Health and Quality of Life Outcomes
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Discover Psychology (Jun 2024)
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Abstract Aim Receiving a diagnosis of brain stem death poses significant challenges for families. While much research focuses on organ donation in the context of brain stem death, there is a notable dearth of studies examining the experiences of families themselves. The aim of this review is to explore the experiences of families facing brain stem death. Design Systematic review. Method A narrative synthesis was conducted, drawing on 11 studies that employed both qualitative and quantitative methodologies. The search encompassed four electronic databases: AHMED (Allied and Complementary Medicine), Emcare (1995-present), MEDLINE (Ovid), and APA PsycInfo (Ovid). Due to the limited research on this topic, no restrictions were placed on the publication dates. Results The synthesis revealed five main themes: The Unexpected Prognosis, Coming to Terms with Brain Stem Death—Grieving Process, Observing Brain Stem Death Testing, The Impact of Staff on Families’ Experience, and The Lasting Impact. Conclusion The review underscores the pervasive lack of understanding among families regarding the diagnosis and process of brain stem death, as well as the short- and long-term distress it can engender. There is a clear imperative to establish national or international protocols for brain stem death, ensuring more effective and consistent support for affected families.
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1 Department of Anesthesiology and Pain Medicine, Inje University Seoul Paik Hospital, Seoul, Korea
2 Department of Anesthesiology and Pain Medicine, Chung-Ang University College of Medicine, Seoul, Korea
Systematic reviews and meta-analyses present results by combining and analyzing data from different studies conducted on similar research topics. In recent years, systematic reviews and meta-analyses have been actively performed in various fields including anesthesiology. These research methods are powerful tools that can overcome the difficulties in performing large-scale randomized controlled trials. However, the inclusion of studies with any biases or improperly assessed quality of evidence in systematic reviews and meta-analyses could yield misleading results. Therefore, various guidelines have been suggested for conducting systematic reviews and meta-analyses to help standardize them and improve their quality. Nonetheless, accepting the conclusions of many studies without understanding the meta-analysis can be dangerous. Therefore, this article provides an easy introduction to clinicians on performing and understanding meta-analyses.
A systematic review collects all possible studies related to a given topic and design, and reviews and analyzes their results [ 1 ]. During the systematic review process, the quality of studies is evaluated, and a statistical meta-analysis of the study results is conducted on the basis of their quality. A meta-analysis is a valid, objective, and scientific method of analyzing and combining different results. Usually, in order to obtain more reliable results, a meta-analysis is mainly conducted on randomized controlled trials (RCTs), which have a high level of evidence [ 2 ] ( Fig. 1 ). Since 1999, various papers have presented guidelines for reporting meta-analyses of RCTs. Following the Quality of Reporting of Meta-analyses (QUORUM) statement [ 3 ], and the appearance of registers such as Cochrane Library’s Methodology Register, a large number of systematic literature reviews have been registered. In 2009, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 4 ] was published, and it greatly helped standardize and improve the quality of systematic reviews and meta-analyses [ 5 ].
Levels of evidence.
In anesthesiology, the importance of systematic reviews and meta-analyses has been highlighted, and they provide diagnostic and therapeutic value to various areas, including not only perioperative management but also intensive care and outpatient anesthesia [6–13]. Systematic reviews and meta-analyses include various topics, such as comparing various treatments of postoperative nausea and vomiting [ 14 , 15 ], comparing general anesthesia and regional anesthesia [ 16 – 18 ], comparing airway maintenance devices [ 8 , 19 ], comparing various methods of postoperative pain control (e.g., patient-controlled analgesia pumps, nerve block, or analgesics) [ 20 – 23 ], comparing the precision of various monitoring instruments [ 7 ], and meta-analysis of dose-response in various drugs [ 12 ].
Thus, literature reviews and meta-analyses are being conducted in diverse medical fields, and the aim of highlighting their importance is to help better extract accurate, good quality data from the flood of data being produced. However, a lack of understanding about systematic reviews and meta-analyses can lead to incorrect outcomes being derived from the review and analysis processes. If readers indiscriminately accept the results of the many meta-analyses that are published, incorrect data may be obtained. Therefore, in this review, we aim to describe the contents and methods used in systematic reviews and meta-analyses in a way that is easy to understand for future authors and readers of systematic review and meta-analysis.
It is easy to confuse systematic reviews and meta-analyses. A systematic review is an objective, reproducible method to find answers to a certain research question, by collecting all available studies related to that question and reviewing and analyzing their results. A meta-analysis differs from a systematic review in that it uses statistical methods on estimates from two or more different studies to form a pooled estimate [ 1 ]. Following a systematic review, if it is not possible to form a pooled estimate, it can be published as is without progressing to a meta-analysis; however, if it is possible to form a pooled estimate from the extracted data, a meta-analysis can be attempted. Systematic reviews and meta-analyses usually proceed according to the flowchart presented in Fig. 2 . We explain each of the stages below.
Flowchart illustrating a systematic review.
A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies. Here, the definition of the word “similar” is not made clear, but when selecting a topic for the meta-analysis, it is essential to ensure that the different studies present data that can be combined. If the studies contain data on the same topic that can be combined, a meta-analysis can even be performed using data from only two studies. However, study selection via a systematic review is a precondition for performing a meta-analysis, and it is important to clearly define the Population, Intervention, Comparison, Outcomes (PICO) parameters that are central to evidence-based research. In addition, selection of the research topic is based on logical evidence, and it is important to select a topic that is familiar to readers without clearly confirmed the evidence [ 24 ].
In systematic reviews, prior registration of a detailed research plan is very important. In order to make the research process transparent, primary/secondary outcomes and methods are set in advance, and in the event of changes to the method, other researchers and readers are informed when, how, and why. Many studies are registered with an organization like PROSPERO ( http://www.crd.york.ac.uk/PROSPERO/ ), and the registration number is recorded when reporting the study, in order to share the protocol at the time of planning.
Information is included on the study design, patient characteristics, publication status (published or unpublished), language used, and research period. If there is a discrepancy between the number of patients included in the study and the number of patients included in the analysis, this needs to be clearly explained while describing the patient characteristics, to avoid confusing the reader.
In order to secure proper basis for evidence-based research, it is essential to perform a broad search that includes as many studies as possible that meet the inclusion and exclusion criteria. Typically, the three bibliographic databases Medline, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) are used. In domestic studies, the Korean databases KoreaMed, KMBASE, and RISS4U may be included. Effort is required to identify not only published studies but also abstracts, ongoing studies, and studies awaiting publication. Among the studies retrieved in the search, the researchers remove duplicate studies, select studies that meet the inclusion/exclusion criteria based on the abstracts, and then make the final selection of studies based on their full text. In order to maintain transparency and objectivity throughout this process, study selection is conducted independently by at least two investigators. When there is a inconsistency in opinions, intervention is required via debate or by a third reviewer. The methods for this process also need to be planned in advance. It is essential to ensure the reproducibility of the literature selection process [ 25 ].
However, well planned the systematic review or meta-analysis is, if the quality of evidence in the studies is low, the quality of the meta-analysis decreases and incorrect results can be obtained [ 26 ]. Even when using randomized studies with a high quality of evidence, evaluating the quality of evidence precisely helps determine the strength of recommendations in the meta-analysis. One method of evaluating the quality of evidence in non-randomized studies is the Newcastle-Ottawa Scale, provided by the Ottawa Hospital Research Institute 1) . However, we are mostly focusing on meta-analyses that use randomized studies.
If the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system ( http://www.gradeworkinggroup.org/ ) is used, the quality of evidence is evaluated on the basis of the study limitations, inaccuracies, incompleteness of outcome data, indirectness of evidence, and risk of publication bias, and this is used to determine the strength of recommendations [ 27 ]. As shown in Table 1 , the study limitations are evaluated using the “risk of bias” method proposed by Cochrane 2) . This method classifies bias in randomized studies as “low,” “high,” or “unclear” on the basis of the presence or absence of six processes (random sequence generation, allocation concealment, blinding participants or investigators, incomplete outcome data, selective reporting, and other biases) [ 28 ].
The Cochrane Collaboration’s Tool for Assessing the Risk of Bias [ 28 ]
Domain | Support of judgement | Review author’s judgement |
---|---|---|
Sequence generation | Describe the method used to generate the allocation sequence in sufficient detail to allow for an assessment of whether it should produce comparable groups. | Selection bias (biased allocation to interventions) due to inadequate generation of a randomized sequence. |
Allocation concealment | Describe the method used to conceal the allocation sequence in sufficient detail to determine whether intervention allocations could have been foreseen in advance of, or during, enrollment. | Selection bias (biased allocation to interventions) due to inadequate concealment of allocations prior to assignment. |
Blinding | Describe all measures used, if any, to blind study participants and personnel from knowledge of which intervention a participant received. | Performance bias due to knowledge of the allocated interventions by participants and personnel during the study. |
Describe all measures used, if any, to blind study outcome assessors from knowledge of which intervention a participant received. | Detection bias due to knowledge of the allocated interventions by outcome assessors. | |
Incomplete outcome data | Describe the completeness of outcome data for each main outcome, including attrition and exclusions from the analysis. State whether attrition and exclusions were reported, the numbers in each intervention group, reasons for attrition/exclusions where reported, and any re-inclusions in analyses performed by the review authors. | Attrition bias due to amount, nature, or handling of incomplete outcome data. |
Selective reporting | State how the possibility of selective outcome reporting was examined by the review authors, and what was found. | Reporting bias due to selective outcome reporting. |
Other bias | State any important concerns about bias not addressed in the other domains in the tool. | Bias due to problems not covered elsewhere in the table. |
If particular questions/entries were prespecified in the reviews protocol, responses should be provided for each question/entry. |
Two different investigators extract data based on the objectives and form of the study; thereafter, the extracted data are reviewed. Since the size and format of each variable are different, the size and format of the outcomes are also different, and slight changes may be required when combining the data [ 29 ]. If there are differences in the size and format of the outcome variables that cause difficulties combining the data, such as the use of different evaluation instruments or different evaluation timepoints, the analysis may be limited to a systematic review. The investigators resolve differences of opinion by debate, and if they fail to reach a consensus, a third-reviewer is consulted.
The aim of a meta-analysis is to derive a conclusion with increased power and accuracy than what could not be able to achieve in individual studies. Therefore, before analysis, it is crucial to evaluate the direction of effect, size of effect, homogeneity of effects among studies, and strength of evidence [ 30 ]. Thereafter, the data are reviewed qualitatively and quantitatively. If it is determined that the different research outcomes cannot be combined, all the results and characteristics of the individual studies are displayed in a table or in a descriptive form; this is referred to as a qualitative review. A meta-analysis is a quantitative review, in which the clinical effectiveness is evaluated by calculating the weighted pooled estimate for the interventions in at least two separate studies.
The pooled estimate is the outcome of the meta-analysis, and is typically explained using a forest plot ( Figs. 3 and and4). 4 ). The black squares in the forest plot are the odds ratios (ORs) and 95% confidence intervals in each study. The area of the squares represents the weight reflected in the meta-analysis. The black diamond represents the OR and 95% confidence interval calculated across all the included studies. The bold vertical line represents a lack of therapeutic effect (OR = 1); if the confidence interval includes OR = 1, it means no significant difference was found between the treatment and control groups.
Forest plot analyzed by two different models using the same data. (A) Fixed-effect model. (B) Random-effect model. The figure depicts individual trials as filled squares with the relative sample size and the solid line as the 95% confidence interval of the difference. The diamond shape indicates the pooled estimate and uncertainty for the combined effect. The vertical line indicates the treatment group shows no effect (OR = 1). Moreover, if the confidence interval includes 1, then the result shows no evidence of difference between the treatment and control groups.
Forest plot representing homogeneous data.
In data analysis, outcome variables can be considered broadly in terms of dichotomous variables and continuous variables. When combining data from continuous variables, the mean difference (MD) and standardized mean difference (SMD) are used ( Table 2 ).
Summary of Meta-analysis Methods Available in RevMan [ 28 ]
Type of data | Effect measure | Fixed-effect methods | Random-effect methods |
---|---|---|---|
Dichotomous | Odds ratio (OR) | Mantel-Haenszel (M-H) | Mantel-Haenszel (M-H) |
Inverse variance (IV) | Inverse variance (IV) | ||
Peto | |||
Risk ratio (RR), | Mantel-Haenszel (M-H) | Mantel-Haenszel (M-H) | |
Risk difference (RD) | Inverse variance (IV) | Inverse variance (IV) | |
Continuous | Mean difference (MD), Standardized mean difference (SMD) | Inverse variance (IV) | Inverse variance (IV) |
The MD is the absolute difference in mean values between the groups, and the SMD is the mean difference between groups divided by the standard deviation. When results are presented in the same units, the MD can be used, but when results are presented in different units, the SMD should be used. When the MD is used, the combined units must be shown. A value of “0” for the MD or SMD indicates that the effects of the new treatment method and the existing treatment method are the same. A value lower than “0” means the new treatment method is less effective than the existing method, and a value greater than “0” means the new treatment is more effective than the existing method.
When combining data for dichotomous variables, the OR, risk ratio (RR), or risk difference (RD) can be used. The RR and RD can be used for RCTs, quasi-experimental studies, or cohort studies, and the OR can be used for other case-control studies or cross-sectional studies. However, because the OR is difficult to interpret, using the RR and RD, if possible, is recommended. If the outcome variable is a dichotomous variable, it can be presented as the number needed to treat (NNT), which is the minimum number of patients who need to be treated in the intervention group, compared to the control group, for a given event to occur in at least one patient. Based on Table 3 , in an RCT, if x is the probability of the event occurring in the control group and y is the probability of the event occurring in the intervention group, then x = c/(c + d), y = a/(a + b), and the absolute risk reduction (ARR) = x − y. NNT can be obtained as the reciprocal, 1/ARR.
Calculation of the Number Needed to Treat in the Dichotomous table
Event occurred | Event not occurred | Sum | |
---|---|---|---|
Intervention | A | B | a + b |
Control | C | D | c + d |
In order to analyze effect size, two types of models can be used: a fixed-effect model or a random-effect model. A fixed-effect model assumes that the effect of treatment is the same, and that variation between results in different studies is due to random error. Thus, a fixed-effect model can be used when the studies are considered to have the same design and methodology, or when the variability in results within a study is small, and the variance is thought to be due to random error. Three common methods are used for weighted estimation in a fixed-effect model: 1) inverse variance-weighted estimation 3) , 2) Mantel-Haenszel estimation 4) , and 3) Peto estimation 5) .
A random-effect model assumes heterogeneity between the studies being combined, and these models are used when the studies are assumed different, even if a heterogeneity test does not show a significant result. Unlike a fixed-effect model, a random-effect model assumes that the size of the effect of treatment differs among studies. Thus, differences in variation among studies are thought to be due to not only random error but also between-study variability in results. Therefore, weight does not decrease greatly for studies with a small number of patients. Among methods for weighted estimation in a random-effect model, the DerSimonian and Laird method 6) is mostly used for dichotomous variables, as the simplest method, while inverse variance-weighted estimation is used for continuous variables, as with fixed-effect models. These four methods are all used in Review Manager software (The Cochrane Collaboration, UK), and are described in a study by Deeks et al. [ 31 ] ( Table 2 ). However, when the number of studies included in the analysis is less than 10, the Hartung-Knapp-Sidik-Jonkman method 7) can better reduce the risk of type 1 error than does the DerSimonian and Laird method [ 32 ].
Fig. 3 shows the results of analyzing outcome data using a fixed-effect model (A) and a random-effect model (B). As shown in Fig. 3 , while the results from large studies are weighted more heavily in the fixed-effect model, studies are given relatively similar weights irrespective of study size in the random-effect model. Although identical data were being analyzed, as shown in Fig. 3 , the significant result in the fixed-effect model was no longer significant in the random-effect model. One representative example of the small study effect in a random-effect model is the meta-analysis by Li et al. [ 33 ]. In a large-scale study, intravenous injection of magnesium was unrelated to acute myocardial infarction, but in the random-effect model, which included numerous small studies, the small study effect resulted in an association being found between intravenous injection of magnesium and myocardial infarction. This small study effect can be controlled for by using a sensitivity analysis, which is performed to examine the contribution of each of the included studies to the final meta-analysis result. In particular, when heterogeneity is suspected in the study methods or results, by changing certain data or analytical methods, this method makes it possible to verify whether the changes affect the robustness of the results, and to examine the causes of such effects [ 34 ].
Homogeneity test is a method whether the degree of heterogeneity is greater than would be expected to occur naturally when the effect size calculated from several studies is higher than the sampling error. This makes it possible to test whether the effect size calculated from several studies is the same. Three types of homogeneity tests can be used: 1) forest plot, 2) Cochrane’s Q test (chi-squared), and 3) Higgins I 2 statistics. In the forest plot, as shown in Fig. 4 , greater overlap between the confidence intervals indicates greater homogeneity. For the Q statistic, when the P value of the chi-squared test, calculated from the forest plot in Fig. 4 , is less than 0.1, it is considered to show statistical heterogeneity and a random-effect can be used. Finally, I 2 can be used [ 35 ].
I 2 , calculated as shown above, returns a value between 0 and 100%. A value less than 25% is considered to show strong homogeneity, a value of 50% is average, and a value greater than 75% indicates strong heterogeneity.
Even when the data cannot be shown to be homogeneous, a fixed-effect model can be used, ignoring the heterogeneity, and all the study results can be presented individually, without combining them. However, in many cases, a random-effect model is applied, as described above, and a subgroup analysis or meta-regression analysis is performed to explain the heterogeneity. In a subgroup analysis, the data are divided into subgroups that are expected to be homogeneous, and these subgroups are analyzed. This needs to be planned in the predetermined protocol before starting the meta-analysis. A meta-regression analysis is similar to a normal regression analysis, except that the heterogeneity between studies is modeled. This process involves performing a regression analysis of the pooled estimate for covariance at the study level, and so it is usually not considered when the number of studies is less than 10. Here, univariate and multivariate regression analyses can both be considered.
Publication bias is the most common type of reporting bias in meta-analyses. This refers to the distortion of meta-analysis outcomes due to the higher likelihood of publication of statistically significant studies rather than non-significant studies. In order to test the presence or absence of publication bias, first, a funnel plot can be used ( Fig. 5 ). Studies are plotted on a scatter plot with effect size on the x-axis and precision or total sample size on the y-axis. If the points form an upside-down funnel shape, with a broad base that narrows towards the top of the plot, this indicates the absence of a publication bias ( Fig. 5A ) [ 29 , 36 ]. On the other hand, if the plot shows an asymmetric shape, with no points on one side of the graph, then publication bias can be suspected ( Fig. 5B ). Second, to test publication bias statistically, Begg and Mazumdar’s rank correlation test 8) [ 37 ] or Egger’s test 9) [ 29 ] can be used. If publication bias is detected, the trim-and-fill method 10) can be used to correct the bias [ 38 ]. Fig. 6 displays results that show publication bias in Egger’s test, which has then been corrected using the trim-and-fill method using Comprehensive Meta-Analysis software (Biostat, USA).
Funnel plot showing the effect size on the x-axis and sample size on the y-axis as a scatter plot. (A) Funnel plot without publication bias. The individual plots are broader at the bottom and narrower at the top. (B) Funnel plot with publication bias. The individual plots are located asymmetrically.
Funnel plot adjusted using the trim-and-fill method. White circles: comparisons included. Black circles: inputted comparisons using the trim-and-fill method. White diamond: pooled observed log risk ratio. Black diamond: pooled inputted log risk ratio.
When reporting the results of a systematic review or meta-analysis, the analytical content and methods should be described in detail. First, a flowchart is displayed with the literature search and selection process according to the inclusion/exclusion criteria. Second, a table is shown with the characteristics of the included studies. A table should also be included with information related to the quality of evidence, such as GRADE ( Table 4 ). Third, the results of data analysis are shown in a forest plot and funnel plot. Fourth, if the results use dichotomous data, the NNT values can be reported, as described above.
The GRADE Evidence Quality for Each Outcome
Quality assessment | Number of patients | Effect | Quality | Importance | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
N | ROB | Inconsistency | Indirectness | Imprecision | Others | Palonosetron (%) | Ramosetron (%) | RR (CI) | |||
PON | 6 | Serious | Serious | Not serious | Not serious | None | 81/304 (26.6) | 80/305 (26.2) | 0.92 (0.54 to 1.58) | Very low | Important |
POV | 5 | Serious | Serious | Not serious | Not serious | None | 55/274 (20.1) | 60/275 (21.8) | 0.87 (0.48 to 1.57) | Very low | Important |
PONV | 3 | Not serious | Serious | Not serious | Not serious | None | 108/184 (58.7) | 107/186 (57.5) | 0.92 (0.54 to 1.58) | Low | Important |
N: number of studies, ROB: risk of bias, PON: postoperative nausea, POV: postoperative vomiting, PONV: postoperative nausea and vomiting, CI: confidence interval, RR: risk ratio, AR: absolute risk.
When Review Manager software (The Cochrane Collaboration, UK) is used for the analysis, two types of P values are given. The first is the P value from the z-test, which tests the null hypothesis that the intervention has no effect. The second P value is from the chi-squared test, which tests the null hypothesis for a lack of heterogeneity. The statistical result for the intervention effect, which is generally considered the most important result in meta-analyses, is the z-test P value.
A common mistake when reporting results is, given a z-test P value greater than 0.05, to say there was “no statistical significance” or “no difference.” When evaluating statistical significance in a meta-analysis, a P value lower than 0.05 can be explained as “a significant difference in the effects of the two treatment methods.” However, the P value may appear non-significant whether or not there is a difference between the two treatment methods. In such a situation, it is better to announce “there was no strong evidence for an effect,” and to present the P value and confidence intervals. Another common mistake is to think that a smaller P value is indicative of a more significant effect. In meta-analyses of large-scale studies, the P value is more greatly affected by the number of studies and patients included, rather than by the significance of the results; therefore, care should be taken when interpreting the results of a meta-analysis.
When performing a systematic literature review or meta-analysis, if the quality of studies is not properly evaluated or if proper methodology is not strictly applied, the results can be biased and the outcomes can be incorrect. However, when systematic reviews and meta-analyses are properly implemented, they can yield powerful results that could usually only be achieved using large-scale RCTs, which are difficult to perform in individual studies. As our understanding of evidence-based medicine increases and its importance is better appreciated, the number of systematic reviews and meta-analyses will keep increasing. However, indiscriminate acceptance of the results of all these meta-analyses can be dangerous, and hence, we recommend that their results be received critically on the basis of a more accurate understanding.
1) http://www.ohri.ca .
2) http://methods.cochrane.org/bias/assessing-risk-bias-included-studies .
3) The inverse variance-weighted estimation method is useful if the number of studies is small with large sample sizes.
4) The Mantel-Haenszel estimation method is useful if the number of studies is large with small sample sizes.
5) The Peto estimation method is useful if the event rate is low or one of the two groups shows zero incidence.
6) The most popular and simplest statistical method used in Review Manager and Comprehensive Meta-analysis software.
7) Alternative random-effect model meta-analysis that has more adequate error rates than does the common DerSimonian and Laird method, especially when the number of studies is small. However, even with the Hartung-Knapp-Sidik-Jonkman method, when there are less than five studies with very unequal sizes, extra caution is needed.
8) The Begg and Mazumdar rank correlation test uses the correlation between the ranks of effect sizes and the ranks of their variances [ 37 ].
9) The degree of funnel plot asymmetry as measured by the intercept from the regression of standard normal deviates against precision [ 29 ].
10) If there are more small studies on one side, we expect the suppression of studies on the other side. Trimming yields the adjusted effect size and reduces the variance of the effects by adding the original studies back into the analysis as a mirror image of each study.
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Systematic Review | Definition, Example & Guide
Systematic review: A review that uses explicit, ... Shemilt I. Inclusion of quasi-experimental studies in systematic reviews of health systems research. Health Policy. 2015; 119 (4):511-521. doi: 10.1016/j.healthpol.2014.10.006. [Google Scholar] 77. Mathes T, Pieper D. Clarifying the distinction between case series and cohort studies in ...
Topic selection and planning. In recent years, there has been an explosion in the number of systematic reviews conducted and published (Chalmers & Fox 2016, Fontelo & Liu 2018, Page et al 2015) - although a systematic review may be an inappropriate or unnecessary research methodology for answering many research questions.Systematic reviews can be inadvisable for a variety of reasons.
In recent years, there has been an explosion in the number of systematic reviews conducted and published (Chalmers & Fox 2016, Fontelo & Liu 2018, Page et al 2015) - although a systematic review may be an inappropriate or unnecessary research methodology for answering many research questions.Systematic reviews can be inadvisable for a variety of reasons.
Systematic reviews are characterized by a methodical and replicable methodology and presentation. They involve a comprehensive search to locate all relevant published and unpublished work on a subject; a systematic integration of search results; and a critique of the extent, nature, and quality of evidence in relation to a particular research question.
Guidelines for writing a systematic review. 1. Introduction. A key feature of any academic activity is to have a sufficient understanding of the subject area under investigation and thus an awareness of previous research. Undertaking a literature review with an analysis of the results on a specific issue is required to demonstrate sufficient ...
Background. A systematic review, as its name suggests, is a systematic way of collecting, evaluating, integrating, and presenting findings from several studies on a specific question or topic.[] A systematic review is a research that, by identifying and combining evidence, is tailored to and answers the research question, based on an assessment of all relevant studies.[2,3] To identify assess ...
Systematic Review | Definition, Examples & Guide - Scribbr
A systematic review identifies and synthesizes all relevant studies that fit prespecified criteria to answer a research question (Lasserson et al. 2019; IOM 2011).What sets a systematic review apart from a narrative review is that it follows consistent, rigorous, and transparent methods established in a protocol in order to minimize bias and errors.
Systematic reviews are characterized by a methodical and replicable methodology and presentation. They involve a comprehensive search to locate all relevant published and unpublished work on a subject; a systematic integration of search results; and a critique of the extent, nature, and quality of evidence in relation to a particular research question. The best reviews synthesize studies to ...
Image: https://pixabay.com Steps to conducting a systematic review: PIECES. P: Planning - the methods of the systematic review are generally decided before conducting it. I: Identifying - searching for studies which match the preset criteria in a systematic manner E: Evaluating - sort all retrieved articles (included or excluded) and assess the risk of bias for each included study
Getting Started - Systematic Reviews and Meta Analysis
A systematic review is a comprehensive review of the literature conducted by a research team using systematic and transparent methods in accordance with reporting guidelines to answer a well-defined research question.
A systematic review is a type of study that synthesises research that has been conducted on a particular topic. Systematic reviews are considered to provide the highest level of evidence on the hierarchy of evidence pyramid. Systematic reviews are conducted following rigorous research methodology. To minimise bias, systematic reviews utilise a ...
1.2.2 What is a systematic review?
Several CDC librarians have special training in conducting literature searches for systematic reviews. Literature searches for systematic reviews can take a few weeks to several months from planning to delivery. Fill out a search request form or contact the Stephen B. Thacker CDC Library by email [email protected] or telephone 404-639-1717.
Systematic reviews involve the application of scientific methods to reduce bias in review of literature. The key components of a systematic review are a well-defined research question, comprehensive literature search to identify all studies that potentially address the question, systematic assembly of the studies that answer the question, critical appraisal of the methodological quality of the ...
When applying research to questions for individual patients or for health policy, one of the challenges is interpreting such apparently conflicting research. A systematic review is a method to systematically identify relevant research, appraise its quality, and synthesize the results.
an explicit, reproducible methodology. a systematic search that attempts to identify all studies that would meet the eligibility criteria. an assessment of the validity of the findings of the included studies, for example through the assessment of the risk of bias. a systematic presentation, and synthesis, of the characteristics and findings of ...
What is a systematic review?
Department of Health Research Methods, Evidence, and Impact; McMaster University. Correspondence to Dr. Romina Brignardello-Petersen, HSC 2C, 1280 Main St W, Hamilton, ON, Canada ... authors of systematic reviews should consider the limitations of the evidence included in their review. The Grading of Recommendations Assessment, Development, and ...
This meta-analysis was a meta-research project; because this study is not a typical meta-analysis, we followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline where applicable. 18 The original protocol was registered in Open Science Framework Because the information we used consisted of publicly ...
According to a systematic review and meta-analysis conducted by Lee et al., the pooled prevalence of the disease was 1 in 3,164 (95% CI: 1 in 2,132-1 in 4,712), and the pooled birth incidence was 1 in 2,662 ... This systematic review highlights the need for further research, including studies that focus on identifying predictors of a positive ...
1. INTRODUCTION. Evidence synthesis is a prerequisite for knowledge translation. 1 A well conducted systematic review (SR), often in conjunction with meta‐analyses (MA) when appropriate, is considered the "gold standard" of methods for synthesizing evidence related to a topic of interest. 2 The central strength of an SR is the transparency of the methods used to systematically search ...
The aim of this systematic review and meta-analysis was to determine the association between IDA and dental caries. The results revealed significantly higher odds (OR 3.54) of dental caries in children affected by IDA compared to those without IDA. ... The importance of future rigorous research is emphasized to strengthen the certainty of the ...
Background: To examine whether lack of measurement invariance (MI) influences mean comparisons among different disease groups, this paper provides (1) a systematic review of MI in generic constructs across chronic conditions and (2) an empirical analysis of MI in the Health Education Impact Questionnaire (heiQ™).Methods: (1) We searched for studies of MI among different chronic conditions in ...
While much research focuses on organ donation in the context of brain stem death, there is a notable dearth of studies examining the experiences of families themselves. The aim of this review is to explore the experiences of families facing brain stem death. Design Systematic review.
Systematic reviews can also demonstrate where knowledge is lacking. This can then be used to guide future research. Systematic reviews are usually carried out in the areas of clinical tests (diagnostic, screening, and prognostic), public health interventions, adverse (harm) effects, economic (cost) evaluations, and how and why interventions work.
Objective:There are significant temporal and financial barriers for individuals with personality disorders (PD) receiving evidence-based psychological treatments. Emerging research indicates Group Schema Therapy (GST) may be an accessible, efficient, and cost-effective PD intervention, however, there has been no synthesis of the available evidence to date. This review therefore aimed to ...
It is easy to confuse systematic reviews and meta-analyses. A systematic review is an objective, reproducible method to find answers to a certain research question, by collecting all available studies related to that question and reviewing and analyzing their results. A meta-analysis differs from a systematic review in that it uses statistical ...