Dataset Information

A re-analysis of the Cochrane Library data: the dangers of unobserved heterogeneity in meta-analyses.

ABSTRACT:

Background

Heterogeneity has a key role in meta-analysis methods and can greatly affect conclusions. However, true levels of heterogeneity are unknown and often researchers assume homogeneity. We aim to: a) investigate the prevalence of unobserved heterogeneity and the validity of the assumption of homogeneity; b) assess the performance of various meta-analysis methods; c) apply the findings to published meta-analyses.

Methods and findings

We accessed 57,397 meta-analyses, available in the Cochrane Library in August 2012. Using simulated data we assessed the performance of various meta-analysis methods in different scenarios. The prevalence of a zero heterogeneity estimate in the simulated scenarios was compared with that in the Cochrane data, to estimate the degree of unobserved heterogeneity in the latter. We re-analysed all meta-analyses using all methods and assessed the sensitivity of the statistical conclusions. Levels of unobserved heterogeneity in the Cochrane data appeared to be high, especially for small meta-analyses. A bootstrapped version of the DerSimonian-Laird approach performed best in both detecting heterogeneity and in returning more accurate overall effect estimates. Re-analysing all meta-analyses with this new method we found that in cases where heterogeneity had originally been detected but ignored, 17-20% of the statistical conclusions changed. Rates were much lower where the original analysis did not detect heterogeneity or took it into account, between 1% and 3%.

Conclusions

When evidence for heterogeneity is lacking, standard practice is to assume homogeneity and apply a simpler fixed-effect meta-analysis. We find that assuming homogeneity often results in a misleading analysis, since heterogeneity is very likely present but undetected. Our new method represents a small improvement but the problem largely remains, especially for very small meta-analyses. One solution is to test the sensitivity of the meta-analysis conclusions to assumed moderate and large degrees of heterogeneity. Equally, whenever heterogeneity is detected, it should not be ignored.

SUBMITTER: Kontopantelis E

PROVIDER: S-EPMC3724681 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

A re-analysis of the Cochrane Library data: the dangers of unobserved heterogeneity in meta-analyses.

Kontopantelis Evangelos E Springate David A DA Reeves David D

PloS one 20130726 7

<h4>Background</h4>Heterogeneity has a key role in meta-analysis methods and can greatly affect conclusions. However, true levels of heterogeneity are unknown and often researchers assume homogeneity. We aim to: a) investigate the prevalence of unobserved heterogeneity and the validity of the assumption of homogeneity; b) assess the performance of various meta-analysis methods; c) apply the findings to published meta-analyses.<h4>Methods and findings</h4>We accessed 57,397 meta-analyses, availab ...[more]

PMID: 23922860

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Project description:BackgroundMany 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.MethodsOur 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.ResultsBetween-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.ConclusionsMeta-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.

Project description:Meta-analyses conducted via the Cochrane Collaboration adhere to strict methodological and reporting standards aiming to minimize bias, maximize transparency/reproducibility, and improve the accuracy of summarized data. Whether this results in differences in the results reported by meta-analyses on the same topic conducted outside the Cochrane Collaboration is an open question.We conducted a matched-pair analysis with individual meta-analyses as the unit of analysis, comparing Cochrane and non-Cochrane reviews. Using meta-analyses from the cardiovascular literature, we identified pairs that matched on intervention and outcome. The pairs were contrasted in terms of how frequently results disagreed between the Cochrane and non-Cochrane reviews, whether effect sizes and statistical precision differed systematically, and how these differences related to the frequency of secondary citations of those reviews.Our search yielded 40 matched pairs of reviews. The two sets were similar in terms of which was first to publication, how many studies were included, and average sample sizes. The paired reviews included a total of 344 individual clinical trials: 111 (32.3%) studies were included only in a Cochrane review, 104 (30.2%) only in a non-Cochrane review, and 129 (37.5%) in both. Stated another way, 62.5% of studies were only included in one or the other meta-analytic literature. Overall, 37.5% of pairs had discrepant results. The most common involved shifts in the width of 95% confidence intervals that would yield a different statistical interpretation of the significance of results (7 pairs). Additionally, 20% differed in the direction of the summary effect size (5 pairs) or reported greater than a 2-fold difference in its magnitude (3 pairs). Non-Cochrane reviews reported significantly higher effect sizes (P< 0.001) and lower precision (P<0.001) than matched Cochrane reviews. Reviews reporting an effect size at least 2-fold greater than their matched pair were cited more frequently.Though results between topic-matched Cochrane and non-Cochrane reviews were quite similar, discrepant results were frequent, and the overlap of included studies was surprisingly low. Non-Cochrane reviews report larger effect sizes with lower precision than Cochrane reviews, indicating systematic differences, likely reflective of methodology, between the two types of reviews that could generate different interpretations of the interventions under question.

Project description:BACKGROUND:There is growing interest in evaluating differences in healthcare interventions across routinely collected demographic characteristics. However, individual subgroup analyses in randomized controlled trials are often not prespecified, adjusted for multiple testing, or conducted using the appropriate statistical test for interaction, and therefore frequently lack credibility. Meta-analyses can be used to examine the validity of potential subgroup differences by collating evidence across trials. Here, we characterize the conduct and clinical translation of age-treatment subgroup analyses in Cochrane reviews. METHODS:For a random sample of 928 Cochrane intervention reviews of randomized trials, we determined how often subgroup analyses of age are reported, how often these analyses have a P?<?0.05 from formal interaction testing, how frequently subgroup differences first observed in an individual trial are later corroborated by other trials in the same meta-analysis, and how often statistically significant results are included in commonly used clinical management resources (BMJ Best Practice, UpToDate, Cochrane Clinical Answers, Google Scholar, and Google search). RESULTS:Among 928 Cochrane intervention reviews, 189 (20.4%) included plans to conduct age-treatment subgroup analyses. The vast majority (162 of 189, 85.7%) of the planned analyses were not conducted, commonly because of insufficient trial data. There were 22 reviews that conducted their planned age-treatment subgroup analyses, and another 3 reviews appeared to perform unplanned age-treatment subgroup analyses. These 25 (25 of 928, 2.7%) reviews conducted a total of 97 age-treatment subgroup analyses, of which 65 analyses (in 20 reviews) had non-overlapping subgroup levels. Among the 65 age-treatment subgroup analyses, 14 (21.5%) did not report any formal interaction testing. Seven (10.8%) reported P?<?0.05 from formal age-treatment interaction testing; however, none of these seven analyses were in reviews that discussed the potential biological rationale or clinical significance of the subgroup findings or had results that were included in common clinical practice resources. CONCLUSION:Age-treatment subgroup analyses in Cochrane intervention reviews were frequently planned but rarely conducted, and implications of detected interactions were not discussed in the reviews or mentioned in common clinical resources. When subgroup analyses are performed, authors should report the findings, compare the results to previous studies, and outline any potential impact on clinical care.

Dataset Information

A re-analysis of the Cochrane Library data: the dangers of unobserved heterogeneity in meta-analyses.

Background

Methods and findings

Conclusions

Publications

A re-analysis of the Cochrane Library data: the dangers of unobserved heterogeneity in meta-analyses.

Similar Datasets

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