Journal
STATISTICS IN MEDICINE
Volume 37, Issue 29, Pages 4335-4352Publisher
WILEY
DOI: 10.1002/sim.7925
Keywords
bias; enrichment design; maximum likelihood estimator; prevalence; subgroup analysis; subpopulation selection
Categories
Funding
- National Institute for Health Research [NIHR-SRF-2015-08-001]
- Medical Research Council [MR/M005755/1]
- MRC [MR/M005755/1] Funding Source: UKRI
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Population heterogeneity is frequently observed among patients' treatment responses in clinical trials because of various factors such as clinical background, environmental, and genetic factors. Different subpopulations defined by those baseline factors can lead to differences in the benefit or safety profile of a therapeutic intervention. Ignoring heterogeneity between subpopulations can substantially impact on medical practice. One approach to address heterogeneity necessitates designs and analysis of clinical trials with subpopulation selection. Several types of designs have been proposed for different circumstances. In this work, we discuss a class of designs that allow selection of a predefined subgroup. Using the selection based on the maximum test statistics as the worst-case scenario, we then investigate the precision and accuracy of the maximum likelihood estimator at the end of the study via simulations. We find that the required sample size is chiefly determined by the subgroup prevalence and show in simulations that the maximum likelihood estimator for these designs can be substantially biased.
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