Data dredging is the main pitfall in reaching reliable conclusions from meta-regression. This applies particularly when averages of patient characteristics in each trial are used as covariates in the regression. The associations derived from meta-regressions are observational, and have a weaker interpretation than the causal relationships derived from randomized comparisons. This corresponds to random eects meta-regression. One principal methodological issue is that meta-regression should be weighted to take account of both within-trial variances of treatment eects and the residual between-trial heterogeneity (that is, heterogeneity not explained by the covariates in the regression). Here we summarize recent research focusing on these issues, and consider three published examples of meta-regression in the light of this work.
Abstract: SUMMARY Appropriate methods for meta-regression applied to a set of clinical trials, and the limitations and pitfalls in interpretation, are insuciently recognized.
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