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Exploratory subgroup analysis in clinical trials by model selection
Author(s) -
Rosenkranz Gerd K.
Publication year - 2016
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201500147
Subject(s) - replicate , resampling , selection (genetic algorithm) , estimator , selection bias , subgroup analysis , statistics , econometrics , variance (accounting) , computer science , model selection , clinical trial , data mining , machine learning , artificial intelligence , mathematics , medicine , economics , confidence interval , accounting
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.