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Point estimation in adaptive enrichment designs
Author(s) -
Kunzmann Kevin,
Benner Laura,
Kieser Meinhard
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.7412
Subject(s) - estimator , statistics , subgroup analysis , population , point estimation , interim analysis , mathematics , interim , econometrics , computer science , clinical trial , confidence interval , medicine , environmental health , archaeology , pathology , history
Adaptive enrichment designs are an attractive option for clinical trials that aim at demonstrating efficacy of therapies, which may show different benefit for the full patient population and a prespecified subgroup. In these designs, based on interim data, either the subgroup or the full population is selected for further exploration. When selection is based on efficacy data, this introduces bias to the commonly used maximum likelihood estimator. For the situation of two‐stage designs with a single prespecified subgroup, we present six alternative estimators and investigate their performance in a simulation study. The most consistent reduction of bias over the range of scenarios considered was achieved by a method combining the uniformly minimum variance conditionally unbiased estimator with a conditional moment estimator. Application of the methods is illustrated by a clinical trial example.