Biography: Donglin Zeng (Chinese: 曾冬林) is a professor at the department of Biostatistics at the University of North Carolina. He obtained PhD of Statistics from the University of Michigan in 2001. His research interest includes semiparametric models, machine learning, personalized medicine, causal inference and clinical trials.

Title: Learning Individualized Treatment Rules for Multiple-Domain Latent Outcomes

Abstract: For many mental disorders, latent mental status from multiple-domain psychological or clinical symptoms perform as a better characterization of the underlying disorder status than a simple summary score of the symptoms, and they also serve as more reliable and representative features to differentiate treatment responses. In order to address the complexity and heterogeneity of treatment responses for the mental disorders, we provide a new paradigm for learning optimal individualized treatment rules by modeling patients' latent mental status. Specifically, we optimize a value function defined by the latent states after treatment by exploiting a transformation of the observed symptoms based without modeling the relationship between the latent mental states before and after treatment. The optimal treatment rules are derived using a weighted large margin classifier. We derive the convergence rate of the proposed estimator under the latent models. Simulation studies are conducted to test the performance of the proposed method. Finally, we apply the developed method to study for patients with major depression.