Accounting for baseline differences in meta-analysis

Abstract

Substantial clinical differences between the studies included in a meta-analysis may compromise the overall applicability of the summary estimate.1 Several approaches have been suggested to account for heterogeneity, such as conducting a random effects meta-analysis and computing predictive intervals, and include several study-level characteristics as predictors of the observed effect size.2–4 If individual participant data are available, the investigation of associations between outcome and patient-level characteristics is straightforward by applying regression techniques. However, at the standard pair-wise meta-analysis level, usually only trial-level results are given. This does not always allow researchers to adequately explore the association between predictors and effect sizes, as it is possible that population characteristics do not vary consistently within and/or across trials. One potentially important source of heterogeneity in a meta-analysis is the baseline severity of study participants with respect to the disease under investigation.5 This is evident in mental health trials, for instance, antidepressants appeared to be more effective when administered to severely depressed populations rather than to patients with mild depression.6–12 The possible association between baseline severity and relative effects can be explored via meta-regression, a tool used in meta-analysis to explore the impact of moderator variables or predictors on the study effect size.13 This is straightforward when the required data are available (eg, by using the average score of the participants on a rating scale at the beginning of each study as a predictor for the relative effects). However, particularly for dichotomous outcomes, we usually do not have this type of information and we need to express baseline severity using surrogate predictors. In practice, the underlying or control group risk (defined as the probability for a success in the control arm) is often used as a proxy for a collection of unavailable population and setting characteristics, which might …

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