Abstract: We develop new modeling for personalized treatment for longitudinal studies involving high heterogeneity of treatment effects. Incorporating subjectspecific information into the treatment assignment is crucial since different individuals can 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 that 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.

Key words and phrases: Generalized linear mixed model, penalized quasi-likelihood, personalized treatment, quadratic inference functions, random forest.