An approach to confirmatory testing of subpopulations in clinical trials.

In oncology studies with immunotherapies, populations of "super-responders" (patients in whom the treatment works particularly well) are often suspected to be related to biomarkers. In this paper, we explore various ways of confirmatory statistical hypothesis testing for joint inference on the subpopulation of putative "super-responders" and the full study population. A model-based testing framework is proposed, which allows to define, up-front, the strength of evidence required from both full and subpopulations in terms of clinical efficacy. This framework is based on a two-way analysis of variance (ANOVA) model with an interaction in combination with multiple comparison procedures. The ease of implementation of this model-based approach is emphasized and details are provided for the practitioner who would like to adopt this approach. The discussion is exemplified by a hypothetical trial that uses an immune-marker in oncology to define the subpopulation and tumor growth as the primary endpoint.