Journal
STATISTICS IN MEDICINE
Volume 37, Issue 11, Pages 1830-1845Publisher
WILEY
DOI: 10.1002/sim.7638
Keywords
clinical trial; EM algorithm; Neyman-Pearson classification; profile likelihood; semiparametric model; subgroup
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In analyzing clinical trials, one important objective is to classify the patients into treatment-favorable and nonfavorable subgroups. Existing parametric methods are not robust, and the commonly used classification rules ignore the fact that the implications of treatment-favorable and nonfavorable subgroups can be different. To address these issues, we propose a semiparametric model, incorporating both our knowledge and uncertainty about the true model. The Wald statistics is used to test the existence of subgroups, while the Neyman-Pearson rule to classify each subject. Asymptotic properties are derived, simulation studies are conducted to evaluate the performance of the method, and then method is used to analyze a real-world trial data.
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