A unified approach for subgroup identification and individualized treatment recommendation with applications to randomized control trials and observational studies
Issue title: Machine Learning in Applied Statistics
Affiliations: [a] Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, USA | [b] Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA
Correspondence:
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Corresponding author: Haoda Fu, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA. E-mail: [email protected].
Abstract: Precision medicine is important in the new era of medical product development. It focuses on optimizing healthcare decision for each individual patient based on this subject’s context information. Traditional statistics methods for precision medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials. Its has limited capability to handle observational studies where treatment assignments could depend on covariates. In this paper, we described the limitations of traditional subgroup identification methods, and propose a general framework which connects the subgroup identification methods and individualized treatment recommendation rules. The proposed framework is able to handle two or more than two treatments from both randomized control trials and observation studies. We implement our algorithm in C++, and connect it with R. The performance is evaluated by simulations, and we apply our method to a dataset from a diabetes study.
Keywords: Multiple treatments, observational studies, personalized medicine, randomized control trials, subgroup identification, value function
