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Abstract:
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In clinical trials, one important objective is to classify the patients into treatment-favorable and non-favorable subgroups. Existing parametric methods are non-robust, and the commonly used classification rules do not consider the priority of the treatment-favorable subgroup. To address these issues, we propose a semi-parametric model, with the sub-densities specified non-parametric. For nonparametric mixture identifiability, the sub-density is assumed symmetric, and unimodal to find its nonparametric maximum likelihood estimate. The semi-parametric likelihood ratio statistics is used to test the existence of subgroups, while the Neyman-Pearson rule to classify each subject. Asymptotic properties are derived, simulation studies conducted to evaluate the performance of the method, and then it is used to analyze a real data.
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