Adaptive designs for subpopulation analysis optimizing utility functions.
If the response to treatment depends on genetic biomarkers, it is important to identify predictive biomarkers that define (sub-)populations where the treatment has a positive benefit risk balance. One approach to determine relevant subpopulations are subgroup analyses where the treatment effect is estimated in biomarker positive and biomarker negative groups. Subgroup analyses are challenging because several types of risks are associated with inference on subgroups. On the one hand, by disregarding a relevant subpopulation a treatment option may be missed due to a dilution of the treatment effect in the full population. Furthermore, even if the diluted treatment effect can be demonstrated in an overall population, it is not ethical to treat patients that do not benefit from the treatment when they can be identified in advance. On the other hand, selecting a spurious subpopulation increases the risk to restrict an efficacious treatment to a too narrow fraction of a potential benefiting population. We propose to quantify these risks with utility functions and investigate nonadaptive study designs that allow for inference on subgroups using multiple testing procedures as well as adaptive designs, where subgroups may be selected in an interim analysis. The characteristics of such adaptive and nonadaptive designs are compared for a range of scenarios.
Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria; Competence Center for Clinical Trials, University of Bremen, Linzer Strasse 4, 28359, Bremen, Germany.
Alexandra C
AC
Graf
Martin
M
Posch
Franz
F
Koenig
D015415:Biomarkers / Q000378:metabolism; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic; D006801:Humans; D018570:Risk Assessment; D016896:Treatment Outcome
D015415:Biomarkers
Adaptive design; Enrichment design; Hypothesis selection; Sample size reallocation; Utility function