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Treatment evaluation for a data‐driven subgroup in adaptive enrichment designs of clinical trials
Author(s) -
Zhang Zhiwei,
Chen Ruizhe,
Soon Guoxing,
Zhang Hui
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7497
Subject(s) - subgroup analysis , covariate , interim analysis , clinical trial , computer science , selection (genetic algorithm) , flexibility (engineering) , early stopping , interim , human immunodeficiency virus (hiv) , statistics , medicine , machine learning , meta analysis , mathematics , family medicine , archaeology , artificial neural network , history
Adaptive enrichment designs (AEDs) of clinical trials allow investigators to restrict enrollment to a promising subgroup based on an interim analysis. Most of the existing AEDs deal with a small number of predefined subgroups, which are often unknown at the design stage. The newly developed Simon design offers a great deal of flexibility in subgroup selection (without requiring pre‐defined subgroups) but does not provide a procedure for estimating and testing treatment efficacy for the selected subgroup. This article proposes a 2‐stage AED which does not require predefined subgroups but requires a prespecified algorithm for choosing a subgroup on the basis of baseline covariate information. Having a prespecified algorithm for subgroup selection makes it possible to use cross‐validation and bootstrap methods to correct for the resubstitution bias in estimating treatment efficacy for the selected subgroup. The methods are evaluated and compared in a simulation study mimicking actual clinical trials of human immunodeficiency virus infection.