Detecting heterogeneity of intervention effects using analysis and meta-analysis of differences in variance between arms of a trial
AbstractRandomised controlled trials (RCTs) with continuous outcomes usually only examine mean differences in response between trial arms. If the intervention has heterogeneous effects (e.g. the effect of the intervention differs by individual characteristics), then outcome variances will also differ between arms. However, power of an individual trial to assess heterogeneity is lower than the power to detect the same size of main effect. The aim of this work was to describe and implement methods for examining heterogeneity of effects of interventions, in trials with individual patient data (IPD) and also in meta-analyses using summary data. Several methods for assessing differences in variance were applied using IPD from a single trial, and summary data from two meta-analyses.In the single trial there was agreement between methods, and the difference in variance was largely due to differences in depression at baseline. In two meta-analyses, most individual trials did not show strong evidence of a difference in variance between arms, with wide confidence intervals. However, both meta-analyses showed evidence of greater variance in the control arm, and in one example this was perhaps because mean outcome in the control arm was higher.Low power of individual trials to examine differences in variance can be overcome using meta-analysis. Evidence of differences in variance should be followed-up to identify potential effect modifiers and explore other possible causes such as varying compliance.