학술논문
Individualized treatment rule characterization via a value function surrogate.
Document Type
Academic Journal
Author
Freeman NLB; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, United States.; Browder SE; Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, United States.; McGinigle KL; Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, United States.; Kosorok MR; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, United States.
Source
Publisher: Oxford University Press Country of Publication: United States NLM ID: 0370625 Publication Model: Print Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE
Subject
Language
English
Abstract
Precision medicine is a promising framework for generating evidence to improve health and health care. Yet, a gap persists between the ever-growing number of statistical precision medicine strategies for evidence generation and implementation in real-world clinical settings, and the strategies for closing this gap will likely be context-dependent. In this paper, we consider the specific context of partial compliance to wound management among patients with peripheral artery disease. Using a Gaussian process surrogate for the value function, we show the feasibility of using Bayesian optimization to learn optimal individualized treatment rules. Further, we expand beyond the common precision medicine task of learning an optimal individualized treatment rule to the characterization of classes of individualized treatment rules and show how those findings can be translated into clinical contexts.
(© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)
(© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)