New Methodology and Theory for Optimal Treatment Regimes with Applications to Precision Medicine

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The problem of finding the optimal treatment regime, or a series of sequential treatment regimes, based on individual characteristics has important applications in areas such as precision medicine, government policies, and active labor market interventions. Depending on the application, a treatment can represent a drug, a device, a program, a policy, an intervention, or a strategy. Stimulated by the advancements in fields such as genomics and medical imaging, the last decade has witnessed exciting and remarkable progress in personalized medicine, ranging from treatments for breast cancer to treatments for major depressive disorders. The success of precision medicine depends on the development of accurate and reliable statistical and machine learning tools for estimating the optimal treatment regime given the data collected from randomized experiments or observational studies. This project will develop novel methodology, theory, and algorithms with the potential to significantly advance the state of the art in statistical estimation and inference for optimal treatment regimes. The proposed research will significantly enhance the availability of statistical methodology and theory for static or dynamic optimal treatment regimes estimation. A systematic framework for estimating optimal treatment regimes using a new quantile criterion for a variety of scenarios will be developed. The research will focus on both one-stage (static) treatment regimes and dynamic treatment regimes, the latter allowing for treatments to vary with time. In addition, the research will address completely observed responses and randomly censored responses (e.g., survival times), randomized trials and observational studies, and doubly robust estimation. The framework will also be extended to alternative criteria such as Gini's mean difference. This project will significantly advance the theoretical foundations of a large class of robust estimators of optimal treatment regimes. Furthermore, it addresses the challenging and important problem of developing new methodology and algorithms to identity important variables for optimal treatment regime estimation in the high-dimensional setting. The investigator will develop software packages and make them freely available to the research community. Students from minority groups will be especially encouraged to participate in the proposed project.