Treatment evaluation for a data-driven subgroup in adaptive enrichment designs of clinical trials.
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.
['Zhang|Zhiwei|Z|http://orcid.org/0000-0002-3187-9180', 'Chen|Ruizhe|R|', 'Soon|Guoxing|G|', 'Zhang|Hui|H|']
D000465:Algorithms D056808:Biostatistics D002986:Clinical Trials as Topic D003198:Computer Simulation D015658:HIV Infections D006801:Humans D015233:Models, Statistical D016032:Randomized Controlled Trials as Topic D012044:Regression Analysis D012107:Research Design D018709:Statistics, Nonparametric D016896:Treatment Outcome
cross-validation bootstrap precision medicine predictive biomarker subgroup analysis treatment effect heterogeneity