title: Exploratory subgroup analysis in clinical trials by model selection.
abstract: The interest in individualized medicines and upcoming or renewed regulatory requests to assess treatment effects in subgroups of confirmatory trials requires statistical methods that account for selection uncertainty and selection bias after having performed the search for meaningful subgroups. The challenge is to judge the strength of the apparent findings after mining the same data to discover them. In this paper, we describe a resampling approach that allows to replicate the subgroup finding process many times. The replicates are used to adjust the effect estimates for selection bias and to provide variance estimators that account for selection uncertainty. A simulation study provides some evidence of the performance of the method and an example from oncology illustrates its use.
authors.affiliation: Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria. gerd.rosenkranz@meduniwien.ac.at.|Novartis Pharma AG, Novartis Campus, CH-4002 Basel, Switzerland. gerd.rosenkranz@meduniwien.ac.at.
authors.forename: Gerd K
authors.initials: GK
authors.lastname: Rosenkranz
mesh_terms: D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D009369:Neoplasms / Q000401:mortality; D057285:Precision Medicine / Q000379:methods*; D035501:Uncertainty
keywords: Bias reduction; Bootstrap; Estimation after selection; Selection bias; Selection uncertainty; Selective inference; Subgroup selection