000			02833nam a22003737a 4500
008			201231s20202020 xxu||||| |||| 00| 0 eng d
022			_a0006-341X
024			_a10.1111/biom.13406 [doi]
040			_aOvid MEDLINE(R)
099			_a33215693
245			_aEstimating the optimal individualized treatment rule from a cost-effectiveness perspective.
251			_aBiometrics. 78(1):337-351, 2022 Mar.
252			_aBiometrics. 78(1):337-351, 2022 Mar.
252			_zBiometrics. 2020 Nov 20
253			_aBiometrics
260			_c2022
260			_fFY2021
265			_sppublish
266			_d2020-12-31
268			_aBiometrics. 2020 Nov 20
520			_aOptimal individualized treatment rules (ITRs) provide customized treatment recommendations based on subject characteristics to maximize clinical benefit in accordance with the objectives in precision medicine. As a result, there is growing interest in developing statistical tools for estimating optimal ITRs in evidence-based research. In health economic perspectives, policy makers consider the tradeoff between health gains and incremental costs of interventions to set priorities and allocate resources. However, most work on ITRs has focused on maximizing the effectiveness of treatment without considering costs. In this paper, we jointly consider the impact of effectiveness and cost on treatment decisions and define ITRs under a composite-outcome setting, so that we identify the most cost-effective ITR that accounts for individual-level heterogeneity through direct optimization. In particular, we propose a decision-tree-based statistical learning algorithm that uses a net-monetary-benefit-based reward to provide nonparametric estimations of the optimal ITR. We provide several approaches to estimating the reward underlying the ITR as a function of subject characteristics. We present the strengths and weaknesses of each approach and provide practical guidelines by comparing their performance in simulation studies. We illustrate the top-performing approach from our simulations by evaluating the projected 15-year personalized cost-effectiveness of the intensive blood pressure control of the Systolic Blood Pressure Intervention Trial (SPRINT) study. Copyright (c) 2020 The International Biometric Society.
546			_aEnglish
650			_a*Algorithms
650			_a*Precision Medicine
650			_aComputer Simulation
650			_aCost-Benefit Analysis
650			_aResearch Design
651			_aMedStar Heart & Vascular Institute
657			_aJournal Article
700			_aWeintraub, William S
790			_aBellows BK, Bress AP, Greene TH, Moran AE, Sauer BC, Shen J, Weintraub WS, Xu Y, Zhang Y
856			_uhttps://dx.doi.org/10.1111/biom.13406 _zhttps://dx.doi.org/10.1111/biom.13406
942			_cART _dArticle
999			_c5951 _d5951