title: Tutorial on statistical considerations on subgroup analysis in confirmatory clinical trials.
abstract: Clinical trials target patients who are expected to benefit from a new treatment under investigation. However, the magnitude of the treatment benefit, if it exists, often depends on the patient baseline characteristics. It is therefore important to investigate the consistency of the treatment effect across subgroups to ensure a proper interpretation of positive study findings in the overall population. Such assessments can provide guidance on how the treatment should be used. However, great care has to be taken when interpreting consistency results. An observed heterogeneity in treatment effect across subgroups can arise because of chance alone, whereas true heterogeneity may be difficult to detect by standard statistical tests because of their low power. This tutorial considers issues related to subgroup analyses and their impact on the interpretation of findings of completed trials that met their main objectives. In addition, we provide guidance on the design and analysis of clinical trials that account for the expected heterogeneity of treatment effects across subgroups by establishing treatment benefit in a pre-defined targeted subgroup and/or the overall population. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Division of Biometrics III, Office of Biostatistics, OTS, CDER, FDA, Silver Spring, MD, 20993, U.S.A.
authors.forename: Mohamed
authors.initials: M
authors.lastname: Alosh
authors.affiliation: Office of Biostatistics, OTS, CDER, FDA, Silver Spring, MD, 20993, U.S.A.
authors.forename: Mohammad F
authors.initials: MF
authors.lastname: Huque
authors.affiliation: Statistical Methodology and Consulting, Novartis, Basel, Switzerland.|Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Wien, Austria.
authors.forename: Frank
authors.identifier: 0000-0002-2008-8340
authors.initials: F
authors.lastname: Bretz
authors.affiliation: Mathematics and Statistics Department, Boston University, Boston, MA, U.S.A.
authors.forename: Ralph B
authors.initials: RB
authors.lastname: D'Agostino
mesh_terms: D001241:Aspirin / Q000627:therapeutic use; D000077144:Clopidogrel; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000517:prevention & control; D011486:Protein C / Q000627:therapeutic use; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D011994:Recombinant Proteins / Q000627:therapeutic use; D015203:Reproducibility of Results; D018805:Sepsis / Q000188:drug therapy / Q000401:mortality; D013223:Statistics as Topic; D020521:Stroke / Q000517:prevention & control; D016019:Survival Analysis; D013988:Ticlopidine / Q000031:analogs & derivatives / Q000627:therapeutic use; D016896:Treatment Outcome
chemical_list: D011486:Protein C; D011994:Recombinant Proteins; D000077144:Clopidogrel; C421124:drotrecogin alfa activated; D013988:Ticlopidine; D001241:Aspirin
keywords: negative subgroup treatment effect; supportive subgroup findings; targeted subgroup; treatment-by-subgroup interaction