Estimating individualized treatment rules with risk constraint

Individualized treatment rules (ITRs) recommend treatments based on patient‐specific characteristics in order to maximize the expected clinical outcome. At the same time, the risks caused by various adverse events cannot be ignored. In this paper, we propose a method to estimate an optimal ITR that...

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Veröffentlicht in:Biometrics Jg. 76; H. 4; S. 1310 - 1318
Hauptverfasser: Huang, Xinyang, Xu, Jin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Blackwell Publishing Ltd 01.12.2020
Schlagworte:
Algorithms
Constraints
DC programming
individualized treatment rule
multi‐category classification
Optimization
outcome weighted learning
personalized medicine
risk
risk constraint
Risk management
system optimization
DC programming
risk constraint
outcome weighted learning
multi-category classification
individualized treatment rule
personalized medicine
ISSN:0006-341X, 1541-0420, 1541-0420
Online-Zugang:Volltext
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Zusammenfassung:Individualized treatment rules (ITRs) recommend treatments based on patient‐specific characteristics in order to maximize the expected clinical outcome. At the same time, the risks caused by various adverse events cannot be ignored. In this paper, we propose a method to estimate an optimal ITR that maximizes clinical benefit while having the overall risk controlled at a desired level. Our method works for a general setting of multi‐category treatment. The proposed procedure employs two shifted ramp losses to approximate the 0‐1 loss in the objective function and constraint, respectively, and transforms the estimation problem into a difference of convex functions (DC) programming problem. A relaxed DC algorithm is used to solve the nonconvex constrained optimization problem. Simulations and a real data example are used to demonstrate the finite sample performance of the proposed method.
Bibliographie:ObjectType-Article-1
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/biom.13232