Statistical Perspectives on Subgroup Analysis: Testing for Heterogeneity and Evaluating Error Rate for the Complementary Subgroup

Abstract

Substantial heterogeneity in treatment effects across subgroups can cause significant findings in the overall population to be driven predominantly by those of a certain subgroup, thus raising concern on whether the treatment should be prescribed for the least benefitted subgroup. Because of its low power, a nonsignificant interaction test can lead to incorrectly prescribing treatment for the overall population. This article investigates the power of the interaction test and its implications. Also, it investigates the probability of prescribing the treatment to a nonbenefitted subgroup on the basis of a nonsignificant interaction test and other recently proposed criteria.

Key Words:

• Complementary subgroup error rate
• Supportive assessment
• Targeted subgroup, Testing for interaction

ACKNOWLEDGMENTS

We are very grateful to two anonymous referees, an Associate Editor, and Dr. Shein-Chung Chow, Editor-in-Chief, for their constructive comments that led to improvements in the paper. Also, we would like to thank Matthew Guerra, Ph.D., Brian Barkley, and Laura Elizabeth Wiener for their comments on an early version of the manuscript.

This article reflects the views of the authors and should not be construed to represent the FDA’s views or policies.