Treatment evaluation for a data-driven subgroup in adaptive enrichment designs of clinical trials

Zhiwei Zhang*, Ruizhe Chen, Guoxing Soon, Hui Zhang

*Corresponding author for this work

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalStatistics in Medicine
Volume37
Issue number1
DOIs
StatePublished - Jan 15 2018

Keywords

  • bootstrap
  • cross-validation
  • precision medicine
  • predictive biomarker
  • subgroup analysis
  • treatment effect heterogeneity

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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