Optimizing personalized treatments for targeted patient populations across multiple domains
Year: 2024
Type: article
Abstract: 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.
Authors: Yuan Chen, Donglin Zeng, Yuanjia Wang
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Neurological Disorders and Stroke
Awards: G2019886821, G352873457, G5519501531, G6822408782, G775981337, G8060821442, G8983294323, G9000003249, G9000101943, G9000183798, Cancer Center Support Grant, Statistical Methods for Integrating Mixed-type Biomarkers and Phenotypes in Neurodegenerative Disease Modeling, Machine Learning Methods for Optimizing Individualized Treatment Strategies for Precision Psychiatry
OpenAlex ID: W4402896356