A flexible strategy for testing subgroups and overall population.
Subgroup analyses in addition to the total study population analysis are common in clinical trials. However, it is well recognized that findings from subgroup analyses do not provide confirmatory evidence for subgroup treatment effects without placing a priori criteria for ensuring that their findings are scientifically sound. In this paper we address some of the common pitfalls of subgroup analyses. Subgroups analyses inherently have low power for detecting treatment effects. We investigate the power interplay for a subgroup analysis and that for the total study population and list factors that impact the power of a subgroup analysis. Then we introduce a flexible statistical strategy for testing a pre-specified sequence of hypotheses for both the overall and a subgroup. The proposed method strongly controls the familywise Type I error rate and enjoys higher power than other traditional methods. This testing strategy allows testing for a subgroup once a pre-specified degree of consistency in the efficacy findings between the subgroup and the overall study population is met. In addition, it accounts for the dependency between test statistics for the subgroup and the overall study population. We discuss the power performance of this new method and provide significance levels for subgroup analysis. Finally, we illustrate its application through retrospective analysis of data from three published clinical trials.
Alosh
Mohamed
M
Division of Biometrics III, Office of Biostatistics, OTS, CDER, FDA, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA. mohamed.Alosh@fda.hhs.gov
Huque
Mohammad F
MF
D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003218:Condylomata Acuminata / Q000188:drug therapy; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D018401:Sample Size; D016896:Treatment Outcome