Learning individualized treatment rules (ITRs) for a target patient population with mental disorders is confronted with many challenges. First, the target population may be different from the training population that provided data for learning ITRs. Ignoring differences between the training patient data and the target population can result in sub-optimal treatment strategies for the target population. Second, for mental disorders, a patient's underlying mental state is not observed but can be inferred from measures of high-dimensional combinations of symptomatology. Treatment mechanisms are unknown and can be complex, and thus treatment effect moderation can take complicated forms. To address these challenges, we propose a novel method that connects measurement models, efficient weighting schemes, and flexible neural network architecture through latent variables to tailor treatments for a target population. Patients' underlying mental states are represented by a compact set of latent state variables while preserving interpretability. Weighting schemes are designed based on lower-dimensional latent variables to efficiently balance population differences so that biases in learning the latent structure and treatment effects are mitigated. Extensive simulation studies demonstrated consistent superiority of the proposed method and the weighting approach. Applications to two real-world studies of patients with major depressive disorder have shown a broad utility of the proposed method in improving treatment outcomes in the target population.
Keywords: domain adaptation; inverse probability weighting; latent variables; neural network; precision medicine; transfer learning.
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