Doubly robust learning for estimating individualized treatment with censored data

• 成果类型: Article
• 署名作者: Zhao, Y. Q.; Zeng, D.; Laber, E. B.; Song, R.; Yuan, M.; Kosorok, M. R.
• 署名单位: University of Wisconsin System; University of Wisconsin Madison; University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu050
• 发表日期: 2015
• 页码: 151168
• 关键词: Support vector machines; regression-models; TREATMENT REGIMES; CLASSIFICATION

摘要

Individualized treatment rules recommend treatments based on individual patient characteristics in order to maximize clinical benefit. When the clinical outcome of interest is survival time, estimation is often complicated by censoring. We develop nonparametric methods for estimating an optimal individualized treatment rule in the presence of censored data. To adjust for censoring, we propose a doubly robust estimator which requires correct specification of either the censoring model or survival model, but not both; the method is shown to be Fisher consistent when either model is correct. Furthermore, we establish the convergence rate of the expected survival under the estimated optimal individualized treatment rule to the expected survival under the optimal individualized treatment rule. We illustrate the proposed methods using simulation study and data from a Phase III clinical trial on non-small cell lung cancer.