Estimating the optimal individualized treatment rule from a cost-effectiveness perspective. (Record no. 5951)

MARC details
000 -LEADER
fixed length control field 02833nam a22003737a 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 201231s20202020 xxu||||| |||| 00| 0 eng d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 0006-341X
024 ## - OTHER STANDARD IDENTIFIER
Standard number or code 10.1111/biom.13406 [doi]
040 ## - CATALOGING SOURCE
Original cataloging agency Ovid MEDLINE(R)
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
PMID 33215693
245 ## - TITLE STATEMENT
Title Estimating the optimal individualized treatment rule from a cost-effectiveness perspective.
251 ## - Source
Source Biometrics. 78(1):337-351, 2022 Mar.
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Abbreviated source Biometrics. 78(1):337-351, 2022 Mar.
252 ## - Abbreviated Source
Former abbreviated source Biometrics. 2020 Nov 20
253 ## - Journal Name
Journal name Biometrics
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Year 2022
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Manufacturer FY2021
265 ## - SOURCE FOR ACQUISITION/SUBSCRIPTION ADDRESS [OBSOLETE]
Publication status ppublish
266 ## - Date added to catalog
Date added to catalog 2020-12-31
268 ## - Previous citation
-- Biometrics. 2020 Nov 20
520 ## - SUMMARY, ETC.
Abstract Optimal 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 ## - LANGUAGE NOTE
Language note English
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element *Algorithms
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element *Precision Medicine
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Topical term or geographic name entry element Computer Simulation
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Topical term or geographic name entry element Cost-Benefit Analysis
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Topical term or geographic name entry element Research Design
651 ## - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME
Institution MedStar Heart & Vascular Institute
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Medline publication type Journal Article
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Local Authors Weintraub, William S
790 ## - Authors
All authors Bellows BK, Bress AP, Greene TH, Moran AE, Sauer BC, Shen J, Weintraub WS, Xu Y, Zhang Y
856 ## - ELECTRONIC LOCATION AND ACCESS
DOI <a href="https://dx.doi.org/10.1111/biom.13406">https://dx.doi.org/10.1111/biom.13406</a>
Public note https://dx.doi.org/10.1111/biom.13406
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Journal Article
Item type description Article
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection Home library Current library Date acquired Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
          MedStar Authors Catalog MedStar Authors Catalog 12/31/2020   33215693 33215693 12/31/2020 12/31/2020 Journal Article

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