Zhao Y Q, Zeng D, Laber E B, Song R, Yuan M, Kosorok M R
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, 53792, U.S.A.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, U.S.A.
Biometrika. 2015 Mar 1;102(1):151-168. doi: 10.1093/biomet/asu050.
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.
个体化治疗规则根据患者个体特征推荐治疗方案，以实现临床获益最大化。当关注的临床结局为生存时间时，删失往往会使估计变得复杂。我们开发了在存在删失数据的情况下估计最优个体化治疗规则的非参数方法。为了校正删失，我们提出了一种双稳健估计量，它只需要正确设定删失模型或生存模型中的一个，而不需要两者都正确设定；当任一模型正确时，该方法被证明是费舍尔一致的。此外，我们确立了在估计的最优个体化治疗规则下预期生存的收敛速度与最优个体化治疗规则下预期生存的收敛速度。我们通过模拟研究和一项关于非小细胞肺癌的III期临床试验数据来说明所提出的方法。
