VARIATION IN BASE-LINE RISK AS AN EXPLANATION OF HETEROGENEITY IN METAANALYSIS

A relationship of baseline risk to treatment effect size has been sugg ested as a possible explanation of between-study heterogeneity in meta -analyses. To address this question, we develop regression models to e xamine the relationship between the logits (or other response measure) in the intervention and control groups. A weighted least squares (WLS ) approach is described that allows for the heterogeneous sampling var iation in the two groups, together with a correction of the coefficien ts for sampling error. Two approximate maximum likelihood (ML) solutio ns are also obtained, with or without an assumption of equal variances between groups within studies. A closed form ML solution exists with the assumption of equal variances. Both methods appear preferable to a previously suggested regression model of the log odds ratio on the co ntrol event rate; the methods proposed here use the same scale of meas urement for both study groups, and eliminate an artifactual correlatio n in the regression error structure. The ML. approach may be preferabl e because of its symmetric treatment of study groups, but WLS is more easily implemented with standard software. The methods are illustrated with data from meta-analyses on pre-term delivery and on therapies to lower serum cholesterol. (C) 1997 by John Wiley & Sons, Ltd.