Dataset Information

Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews.

ABSTRACT:

Background

Many meta-analyses contain only a small number of studies, which makes it difficult to estimate the extent of between-study heterogeneity. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, and offers advantages over conventional random-effects meta-analysis. To assist in this, we provide empirical evidence on the likely extent of heterogeneity in particular areas of health care.

Methods

Our analyses included 14?886 meta-analyses from the Cochrane Database of Systematic Reviews. We classified each meta-analysis according to the type of outcome, type of intervention comparison and medical specialty. By modelling the study data from all meta-analyses simultaneously, using the log odds ratio scale, we investigated the impact of meta-analysis characteristics on the underlying between-study heterogeneity variance. Predictive distributions were obtained for the heterogeneity expected in future meta-analyses.

Results

Between-study heterogeneity variances for meta-analyses in which the outcome was all-cause mortality were found to be on average 17% (95% CI 10-26) of variances for other outcomes. In meta-analyses comparing two active pharmacological interventions, heterogeneity was on average 75% (95% CI 58-95) of variances for non-pharmacological interventions. Meta-analysis size was found to have only a small effect on heterogeneity. Predictive distributions are presented for nine different settings, defined by type of outcome and type of intervention comparison. For example, for a planned meta-analysis comparing a pharmacological intervention against placebo or control with a subjectively measured outcome, the predictive distribution for heterogeneity is a log-normal (-2.13, 1.58(2)) distribution, which has a median value of 0.12. In an example of meta-analysis of six studies, incorporating external evidence led to a smaller heterogeneity estimate and a narrower confidence interval for the combined intervention effect.

Conclusions

Meta-analysis characteristics were strongly associated with the degree of between-study heterogeneity, and predictive distributions for heterogeneity differed substantially across settings. The informative priors provided will be very beneficial in future meta-analyses including few studies.

SUBMITTER: Turner RM

PROVIDER: S-EPMC3396310 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews.

Turner Rebecca M RM Davey Jonathan J Clarke Mike J MJ Thompson Simon G SG Higgins Julian Pt JP

International journal of epidemiology 20120329 3

<h4>Background</h4>Many meta-analyses contain only a small number of studies, which makes it difficult to estimate the extent of between-study heterogeneity. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, and offers advantages over conventional random-effects meta-analysis. To assist in this, we provide empirical evidence on the likely extent of heterogeneity in particular areas of health care.<h4>Methods</h4>Our analyses included 14 886 meta-analyses from the ...[more]

PMID: 22461129

Similar Datasets

Project description:BackgroundSystematic reviews and meta-analysis of time-to-event outcomes are frequently published within the Cochrane Database of Systematic Reviews (CDSR). However, these outcomes are handled differently across meta-analyses. They can be analysed on the hazard ratio (HR) scale or can be dichotomized and analysed as binary outcomes using effect measures such as odds ratios (OR) or risk ratios (RR). We investigated the impact of reanalysing meta-analyses from the CDSR that used these different effect measures.MethodsWe extracted two types of meta-analysis data from the CDSR: either recorded in a binary form only ("binary"), or in binary form together with observed minus expected and variance statistics ("OEV"). We explored how results for time-to-event outcomes originally analysed as "binary" change when analysed using the complementary log-log (clog-log) link on a HR scale. For the data originally analysed as HRs ("OEV"), we compared these results to analysing them as binary on a HR scale using the clog-log link or using a logit link on an OR scale.ResultsThe pooled HR estimates were closer to 1 than the OR estimates in the majority of meta-analyses. Important differences in between-study heterogeneity between the HR and OR analyses were also observed. These changes led to discrepant conclusions between the OR and HR scales in some meta-analyses. Situations under which the clog-log link performed better than logit link and vice versa were apparent, indicating that the correct choice of the method does matter. Differences between scales arise mainly when event probability is high and may occur via differences in between-study heterogeneity or via increased within-study standard error in the OR relative to the HR analyses.ConclusionsWe identified that dichotomising time-to-event outcomes may be adequate for low event probabilities but not for high event probabilities. In meta-analyses where only binary data are available, the complementary log-log link may be a useful alternative when analysing time-to-event outcomes as binary, however the exact conditions need further exploration. These findings provide guidance on the appropriate methodology that should be used when conducting such meta-analyses.

Project description:BackgroundPrevious research on data extraction methods in systematic reviews has focused on single aspects of the process. We aimed to provide a deeper insight into these methods by analysing a current sample of reviews.MethodsWe included systematic reviews of health interventions in humans published in English. We analysed 75 Cochrane reviews from May and June 2020 and a random sample of non-Cochrane reviews published in the same period and retrieved from Medline. We linked reviews with protocols and study registrations. We collected information on preparing, piloting, and performing data extraction and on use of software to assist review conduct (automation tools). Data were extracted by one author, with 20% extracted in duplicate. Data were analysed descriptively.ResultsOf the 152 included reviews, 77 reported use of a standardized extraction form (51%); 42 provided information on the type of form used (28%); 24 on piloting (16%); 58 on what data was collected (38%); 133 on the extraction method (88%); 107 on resolving disagreements (70%); 103 on methods to obtain additional data or information (68%); 52 on procedures to avoid data errors (34%); and 47 on methods to deal with multiple study reports (31%). Items were more frequently reported in Cochrane than non-Cochrane reviews. The data extraction form used was published in 10 reviews (7%). Use of software was rarely reported except for statistical analysis software and use of RevMan and GRADEpro GDT in Cochrane reviews. Covidence was the most frequent automation tool used: 18 reviews used it for study selection (12%) and 9 for data extraction (6%).ConclusionsReporting of data extraction methods in systematic reviews is limited, especially in non-Cochrane reviews. This includes core items of data extraction such as methods used to manage disagreements. Few reviews currently use software to assist data extraction and review conduct. Our results can serve as a baseline to assess the uptake of such tools in future analyses.

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.

Project description:BACKGROUND:Information on subgroup assessments in systematic reviews (SR) of atrial fibrillation (AF) is limited. This review aims to describe subgroup analyses in AF SRs to inform the design of SRs and randomized trials as well as clinical practice. METHODS:We conducted a cross sectional meta-epidemiological study of Cochrane AF reviews by searching AF (including variants) in the title, abstract, or keyword field without date or language restrictions (Issue 9; September 2018). Two reviewers independently extracted study characteristics to summarize frequency of subgroups pre-specified and conducted and report credibility of subgroup effects claimed. RESULTS:Of 39 Cochrane reviews identified, 17 met inclusion criteria (including 168 reports of 127 randomized trials) and the majority (16; 94.1%) conducted meta-analysis of outcomes. Most (13; 76.5%) planned pre-specified subgroup analyses; 7 of which (41.2%) conducted subgroups. In these 7 reviews, 56 subgroups were planned, 17 (30.4%) conducted and 6 (10.7%) yielded subgroup effects. Variables such as co-morbid disease, stroke risk factors, prior stroke/transient ischemic attack, age, race, and sex represented 44% (24 subgroups) of all planned subgroups (8 conducted; 14.3%); however, information on covariate selection was lacking. Overall, more subgroups were planned than conducted (mean difference (95% CI) 2.3 (1.2-3.5, p?<?0.001)). Of all subgroups conducted, anticoagulant characteristics comprised a third of all subgroup effects (n?=?5, 35.7%). The credibility of subgroups identified (n?=?14) was assessed and less than half (43%) represented one of a small number of pre-specified hypothesis and rarely were effects seen within studies (7%). Of 5 reviews that reported subgroup effects, only 3 discussed subgroup effects as part of the overall conclusions; none discussed credibility of subgroup effects. CONCLUSIONS:This meta-epidemiological review of a subset of Cochrane AF reviews suggests that planning and reporting of subgroup analyses in AF reviews can be improved to better inform clinical management. Most pre-specified subgroup analyses were not performed, important variables (such as stroke, bleeding risk, and other comorbidities) were rarely examined and credibility of subgroup effects claimed was low. Future reviews should aim to identify important subgroups in their protocols and use recommended approaches to test subgroup effects in order to better support clinical decision-making.

Dataset Information

Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews.

Background

Methods

Results

Conclusions

Publications

Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets