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Zhou J, Zhang J, Lu W, et al. On restricted optimal treatment regime estimation for competing risks data. Biostatistics. 2021;22(2):217-232doi: 10.1093/biostatistics/kxz026.
Zhou, J., Zhang, J., Lu, W., & Li, X. (2021). On restricted optimal treatment regime estimation for competing risks data. Biostatistics (Oxford, England), 22(2), 217-232. https://doi.org/10.1093/biostatistics/kxz026
Zhou, Jie, et al. "On restricted optimal treatment regime estimation for competing risks data." Biostatistics (Oxford, England) vol. 22,2 (2021): 217-232. doi: https://doi.org/10.1093/biostatistics/kxz026
Zhou J, Zhang J, Lu W, Li X. On restricted optimal treatment regime estimation for competing risks data. Biostatistics. 2021 Apr 10;22(2):217-232. doi: 10.1093/biostatistics/kxz026. PMID: 31373360; PMCID: PMC8036005.
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Biostatistics. 2021 Apr 10;22(2):217-232. doi: 10.1093/biostatistics/kxz026.

On restricted optimal treatment regime estimation for competing risks data.

Biostatistics (Oxford, England)

Jie Zhou, Jiajia Zhang, Wenbin Lu, Xiaoming Li

Affiliations

  1. Department of Epidemiology and Biostatistics, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA.
  2. Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27695, USA.
  3. Department of Health Promotion, Education, and Behavior University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA.

PMID: 31373360 PMCID: PMC8036005 DOI: 10.1093/biostatistics/kxz026

Abstract

It is well accepted that individualized treatment regimes may improve the clinical outcomes of interest. However, positive treatment effects are often accompanied by certain side effects. Therefore, when choosing the optimal treatment regime for a patient, we need to consider both efficacy and safety issues. In this article, we propose to model time to a primary event of interest and time to severe side effects of treatment by a competing risks model and define a restricted optimal treatment regime based on cumulative incidence functions. The estimation approach is derived using a penalized value search method and investigated through extensive simulations. The proposed method is applied to an HIV dataset obtained from Health Sciences South Carolina, where we minimize the risk of treatment or virologic failures while controlling the risk of serious drug-induced side effects.

© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected].

Keywords: Competing risks data; Cumulative incidence function; Optimal treatment regime; Side effects; Value search method

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