Label‐invariant models for the analysis of meta‐epidemiological data

Kirsty M. Rhodes · David Mawdsley · Rebecca Turner · Hayley E. Jones · Jelena Savović · Julian P. T. Higgins

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Meta-analysis and systematic reviews Statistical Methods in Clinical Trials Statistical Methods in Epidemiology

Abstract

Rich meta-epidemiological data sets have been collected to explore associations between intervention effect estimates and study-level characteristics. Welton et al proposed models for the analysis of meta-epidemiological data, but these models are restrictive because they force heterogeneity among studies with a particular characteristic to be at least as large as that among studies without the characteristic. In this paper we present alternative models that are invariant to the labels defining the 2 categories of studies. To exemplify the methods, we use a collection of meta-analyses in which the Cochrane Risk of Bias tool has been implemented. We first investigate the influence of small trial sample sizes (less than 100 participants), before investigating the influence of multiple methodological flaws (inadequate or unclear sequence generation, allocation concealment, and blinding). We fit both the Welton et al model and our proposed label-invariant model and compare the results. Estimates of mean bias associated with the trial characteristics and of between-trial variances are not very sensitive to the choice of model. Results from fitting a univariable model show that heterogeneity variance is, on average, 88% greater among trials with less than 100 participants. On the basis of a multivariable model, heterogeneity variance is, on average, 25% greater among trials with inadequate/unclear sequence generation, 51% greater among trials with inadequate/unclear blinding, and 23% lower among trials with inadequate/unclear allocation concealment, although the 95% intervals for these ratios are very wide. Our proposed label-invariant models for meta-epidemiological data analysis facilitate investigations of between-study heterogeneity attributable to certain study characteristics.

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References

2011The Cochrane Collaboration's tool for assessing risk of bias in randomised trials 2008A Re-Evaluation of Random-Effects Meta-Analysis 2012Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews 2013The Impact of Study Size on Meta-analyses: Examination of Underpowered Studies in Cochrane Reviews 2012Influence of reported study design characteristics on intervention effect estimates from randomised controlled trials: combined analysis of meta-epidemiological studies.2010Small study effects in meta-analyses of osteoarthritis trials: meta-epidemiological study 2013Influence of trial sample size on treatment effect estimates: meta-epidemiological study 2010The quality of reports of randomised trials in 2000 and 2006: comparative study of articles indexed in PubMed

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