Dataset Information

Impact of missing participant data for dichotomous outcomes on pooled effect estimates in systematic reviews: a protocol for a methodological study.

ABSTRACT:

Background

There is no consensus on how authors conducting meta-analysis should deal with trial participants with missing outcome data. The objectives of this study are to assess in Cochrane and non-Cochrane systematic reviews: (1) which categories of trial participants the systematic review authors consider as having missing participant data (MPD), (2) how trialists reported on participants with missing outcome data in trials, (3) whether systematic reviewer authors actually dealt with MPD in their meta-analyses of dichotomous outcomes consistently with their reported methods, and (4) the impact of different methods of dealing with MPD on pooled effect estimates in meta-analyses of dichotomous outcomes.

Methods/design

We will conduct a methodological study of Cochrane and non-Cochrane systematic reviews. Eligible systematic reviews will include a group-level meta-analysis of a patient-important dichotomous efficacy outcome, with a statistically significant effect estimate. Teams of two reviewers will determine eligibility and subsequently extract information from each eligible systematic review in duplicate and independently, using standardized, pre-piloted forms. The teams will then use a similar process to extract information from the trials included in the meta-analyses of interest. We will assess first which categories of trial participants the systematic reviewers consider as having MPD. Second, we will assess how trialists reported on participants with missing outcome data in trials. Third, we will compare what systematic reviewers report having done, and what they actually did, in dealing with MPD in their meta-analysis. Fourth, we will conduct imputation studies to assess the effects of different methods of dealing with MPD on the pooled effect estimates of meta-analyses. We will specifically calculate for each method (1) the percentage of systematic reviews that lose statistical significance and (2) the mean change of effect estimates across systematic reviews.

Discussion

The impact of different methods of dealing with MPD on pooled effect estimates will help judge the associated risk of bias in systematic reviews. Our findings will inform recommendations regarding what assumptions for MPD should be used to test the robustness of meta-analytical results.

SUBMITTER: Akl EA

PROVIDER: S-EPMC4285551 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Publications

Impact of missing participant data for dichotomous outcomes on pooled effect estimates in systematic reviews: a protocol for a methodological study.

Akl Elie A EA Kahale Lara A LA Agarwal Arnav A Al-Matari Nada N Ebrahim Shanil S Alexander Paul Elias PE Briel Matthias M Brignardello-Petersen Romina R Busse Jason W JW Diab Batoul B Iorio Alfonso A Kwong Joey J Li Ling L Lopes Luciane Cruz LC Mustafa Reem R Neumann Ignacio I Tikkinen Kari A O KA Vandvik Per Olav PO Zhang Yuqing Y Alonso-Coello Pablo P Guyatt Gordon G

Systematic reviews 20141126

<h4>Background</h4>There is no consensus on how authors conducting meta-analysis should deal with trial participants with missing outcome data. The objectives of this study are to assess in Cochrane and non-Cochrane systematic reviews: (1) which categories of trial participants the systematic review authors consider as having missing participant data (MPD), (2) how trialists reported on participants with missing outcome data in trials, (3) whether systematic reviewer authors actually dealt with ...[more]

PMID: 25423894

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Project description:BackgroundSystematic reviews (SRs) with or without meta-analyses (MAs) are widely used in resolving questions in various healthcare areas (such as, traditional Chinese medicine, public health and surgery), and they are the cornerstone of evidence-based healthcare. However, the reliability of SRs is typically influenced by their methodological quality. AMSTAR (A Measurement Tool to Assess Systematic Reviews) and AMSTAR-2 tools can assess the methodological quality of SRs, and the use of AMSTAR has been investigated. However, AMSTAR-2 is now widely used to evaluate the methodological quality of SRs, but the use of AMSTAR-2 for determining the methodological quality of SRs has not yet been investigated and assessed thoroughly. Thus, we designed the present study to investigate the use of AMSTAR-2 in studies that assessed the methodological quality of a sample of SRs with the AMSTAR-2 and provide references to potential users of AMSTAR-2 tool.MethodsFour commonly used electronic databases including PubMed, EmBase, the Cochrane Library, and Web of Science will be searched following a comprehensive search strategy to identify and retrieve studies that have used AMSTAR-2 tool for evaluating the methodological quality of SRs. Two independent authors will retrieve bibliometric information and methodological data, including all author names, time of publication, and journal names, whether a specific score value was given for each item, and whether overall quality assessment was performed. Descriptive statistical analyses will be used to present the study results, e.g., frequencies and percentages, mean and standard deviation (SD) or median and interquartile range (IQR). In addition, subgroup analyses will be performed to identify the methodological differences (e.g., the reporting of study designs included in SRs) between overviews and methodological studies. The risk ratio (RR) with 95% confidence interval (95% CI) will be calculated to measure the methodological differences. Cytoscape 3.7.1 software tool will be used to construct collaboration network maps. Further, Microsoft Office Excel 2016 and Stata 12.0 will be used to manage and analyze data.DiscussionThe results of this study will identify any gaps in the use of AMSTAR-2 and important bibliometric features, such as active researchers and journals, provide guidance to researchers in various healthcare areas (such as, traditional Chinese medicine and public health) for using AMSTAR-2 tool and help them in developing cooperation and submitting their manuscripts.

Project description:ObjectiveTo assess the risk of bias associated with missing outcome data in systematic reviews.DesignImputation study.SettingSystematic reviews.Population100 systematic reviews that included a group level meta-analysis with a statistically significant effect on a patient important dichotomous efficacy outcome.Main outcome measuresMedian percentage change in the relative effect estimate when applying each of the following assumption (four commonly discussed but implausible assumptions (best case scenario, none had the event, all had the event, and worst case scenario) and four plausible assumptions for missing data based on the informative missingness odds ratio (IMOR) approach (IMOR 1.5 (least stringent), IMOR 2, IMOR 3, IMOR 5 (most stringent)); percentage of meta-analyses that crossed the threshold of the null effect for each method; and percentage of meta-analyses that qualitatively changed direction of effect for each method. Sensitivity analyses based on the eight different methods of handling missing data were conducted.Results100 systematic reviews with 653 randomised controlled trials were included. When applying the implausible but commonly discussed assumptions, the median change in the relative effect estimate varied from 0% to 30.4%. The percentage of meta-analyses crossing the threshold of the null effect varied from 1% (best case scenario) to 60% (worst case scenario), and 26% changed direction with the worst case scenario. When applying the plausible assumptions, the median percentage change in relative effect estimate varied from 1.4% to 7.0%. The percentage of meta-analyses crossing the threshold of the null effect varied from 6% (IMOR 1.5) to 22% (IMOR 5) of meta-analyses, and 2% changed direction with the most stringent (IMOR 5).ConclusionEven when applying plausible assumptions to the outcomes of participants with definite missing data, the average change in pooled relative effect estimate is substantive, and almost a quarter (22%) of meta-analyses crossed the threshold of the null effect. Systematic review authors should present the potential impact of missing outcome data on their effect estimates and use this to inform their overall GRADE (grading of recommendations assessment, development, and evaluation) ratings of risk of bias and their interpretation of the results.

Project description:BACKGROUND:Meta-analysis combines multiple independent studies, which can increase power and provide better estimates. However, it is unclear how best to deal with studies with zero events; such studies are also known as double-zero-event studies (DZS). Several statistical methods have been proposed, but the agreement among different approaches has not been systematically assessed using real-world published systematic reviews. METHODS:The agreement of five commonly used methods (i.e., the inverse-variance, Mantel-Haenszel, Peto, Bayesian, and exact methods) was assessed using the Cohen's ? coefficients using 368 meta-analyses with rare events selected from the Cochrane Database of Systematic Reviews. Three continuity corrections, including the correction of a constant 0.5, the treatment arm continuity correction (TACC), and the empirical (EMP) correction, were used to handle DZS when applying inverse-variance and Mantel-Haenszel methods. RESULTS:When the proportion of DZS studies was lower than 50% in a meta-analysis, different methods had moderately high agreement. However, when this proportion was increased to be over 50%, the agreement among the methods decreased to different extents. For the Bayesian, exact, and Peto methods and the inverse-variance and Mantel-Haenszel methods using the EMP correction, their agreement coefficients with the inverse-variance and Mantel-Haenszel methods using a constant 0.5 and TACC decreased from larger than 0.70 to smaller than 0.30. In contrast, the agreement coefficients only decreased slightly among the Bayesian, exact, and Peto methods and the inverse-variance and Mantel-Haenszel methods using the EMP correction. CONCLUSIONS:To utilize all available information and reduce research waste and avoid overestimating the effect, meta-analysts should incorporate DZS, rather than simply removing them. The Peto and other conventional methods with continuity correction should be avoided when the proportion of DZS is extremely high. The exact and Bayesian methods are highly recommended, except when none of the included studies have an event in one or both treatment arms.

Dataset Information

Impact of missing participant data for dichotomous outcomes on pooled effect estimates in systematic reviews: a protocol for a methodological study.

Background

Methods/design

Discussion

Publications

Impact of missing participant data for dichotomous outcomes on pooled effect estimates in systematic reviews: a protocol for a methodological study.

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