Empirical Bayes estimates of subgroup effects in clinical trials

Abstract

At the completion of a clinical trial it is often desirable to compare the treatments within subgroups of patients. The results of the subgroup analysis are usually reported for the subgroups within which sizable treatment differences are found. This practice can lead to an overestimate of the difference between treatments within the subgroups reported. One way of adjusting for this bias is to use empirical Bayes methods which shrink the extreme estimates toward the overall measure of treatment difference. Both point and interval estimates can be obtained. The computations are illustrated with an example using subgroup data from the Lipid Research Clinics Coronary Primary Prevention Trial.