Flexible regression methods for estimating optimal individualized treatment regimes with scalar and functional covariates

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Názov: Flexible regression methods for estimating optimal individualized treatment regimes with scalar and functional covariates
Autori: Kaidi Kong, Li Guan, Zhongzhan Zhang
Zdroj: Statistical Methods in Medical Research. 34:1459-1479
Informácie o vydavateľovi: SAGE Publications, 2025.
Rok vydania: 2025
Popis: In personalized medicine study, how to estimate the optimal individualized treatment regime based on available individual information is a fundamental problem. In recent years, functional data analysis has appeared extensively in medical research, while the optimal individualized treatment regime based on the combination of scalar covariates and functional covariates have rarely been studied and the only few studies are mostly conducted in the context of randomized trials. In this article, we propose a flexible regression-based approach in which the outcome variable is real-valued and the covariates contain multiple scalar covariates and a functional covariate. Our approach is applicable to both randomized trials and observational studies, and the convergence rates of the proposed optimal individualized treatment regime estimators are presented for both situations. Sufficient simulation studies and a real data analysis are conducted to justified the validity of our proposed method.
Druh dokumentu: Article
Jazyk: English
ISSN: 1477-0334
0962-2802
DOI: 10.1177/09622802251340259
Rights: URL: https://journals.sagepub.com/page/policies/text-and-data-mining-license
Prístupové číslo: edsair.doi...........cda7b93f9c2c17f59b71b7e7ff8e6adc
Databáza: OpenAIRE
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  Data: In personalized medicine study, how to estimate the optimal individualized treatment regime based on available individual information is a fundamental problem. In recent years, functional data analysis has appeared extensively in medical research, while the optimal individualized treatment regime based on the combination of scalar covariates and functional covariates have rarely been studied and the only few studies are mostly conducted in the context of randomized trials. In this article, we propose a flexible regression-based approach in which the outcome variable is real-valued and the covariates contain multiple scalar covariates and a functional covariate. Our approach is applicable to both randomized trials and observational studies, and the convergence rates of the proposed optimal individualized treatment regime estimators are presented for both situations. Sufficient simulation studies and a real data analysis are conducted to justified the validity of our proposed method.
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