A Contextual Ranking and Selection Method for Personalized Medicine

• 成果类型: Article
• 署名作者: Du, Jianzhong; Gao, Siyang; Chen, Chun-Hung
• 署名单位: Fudan University; City University of Hong Kong; City University of Hong Kong; George Mason University
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2022.0232
• 发表日期: 2024
• 关键词: personalized medicine; contextual ranking and selection; simulation optimization; OCBA; convergence rate

摘要

Problem definition: Personalized medicine (PM) seeks the best treatment for each patient among a set of available treatment methods. Because a specific treatment does not work well on all patients, traditionally, the best treatment was selected based on the doctor's personal experience and expertise, which is subject to human errors. In the meantime, stochastic models have been well developed in the literature for a lot of major diseases. This gives rise to a simulation-based solution for PM, which uses the simulation tool to evaluate the performance for pairs of treatment and patient biometric characteristics and, based on that, selects the best treatment for each patient characteristic.Methodology/results: In this research, we extend the ranking and selection (R&S) model in simulation-based decision making to solving PM. The biometric characteristics of a patient are treated as a context for R&S, and we call it contextual ranking and selection (CR&S). We consider two formulations of CR&S with small and large context spaces, respectively, and develop new techniques for solving them and identifying the rate optimal budget allocation rules. Based on them, two selection algorithms are proposed, which can be shown to be numerically superior via a set of tests on abstract and real world examples.Managerial implications: This research provides a systematic way of conducting simulation-based decision-making for PM. To improve the overall decision quality for the possible contexts, more simulation efforts should be devoted to contexts in which it is difficult to distinguish between the best treatment and non-best treatments, and our results quantify the optimal trade-off of the simulation efforts between the pairs of contexts and treatments.