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Abstract:
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We develop new modeling for personalized treatment for longitudinal studies involving high heterogeneity of treatment effects. Incorporating subject-specific information into the treatment assignment is crucial since different individuals could react to the same treatment very differently. We estimate unobserved subject-specific treatment effects through conditional random-effects modeling, and apply the random forest algorithm to allocate effective treatments for individuals. The advantage of our approach is that random-effects estimation does not rely on the normality assumption. In theory, we show that the proposed random-effect estimator is consistent and more efficient than the random-effect estimator which ignores correlation information from longitudinal data. Simulation studies and a data example from an HIV clinical trial also confirm that the proposed method can efficiently identify the best treatments for individual patients.
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