Wang Chi, Tan Zhiqiang, Louis Thomas A
Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY 40536, USA.
Biostatistics and Bioinformatics Shared Resource Facility, Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.
Biom Biostat Int J. 2014;1(2). doi: 10.15406/bbij.2014.01.00006. Epub 2014 Sep 17.
Evaluating the effect of a treatment on a time-to-event outcome is the focus of many randomized clinical trials. It is often observed that the treatment effect is heterogeneous, where only a subgroup of the patients may respond to the treatment due to some unknown mechanism such as genetic polymorphism. In this paper, we propose a semiparametric exponential tilt mixture model to estimate the proportion of patients who respond to the treatment and to assess the treatment effect. Our model is a natural extension of parametric mixture models to a semiparametric setting with a time-to-event outcome. We propose a nonparametric maximum likelihood estimation approach for inference and establish related asymptotic properties. Our method is illustrated by a randomized clinical trial on biodegradable polymer-delivered chemotherapy for malignant gliomas patients.
评估治疗对事件发生时间结局的影响是许多随机临床试验的重点。人们经常观察到治疗效果存在异质性，即由于某些未知机制（如基因多态性），只有一部分患者可能对治疗有反应。在本文中，我们提出了一种半参数指数倾斜混合模型，以估计对治疗有反应的患者比例并评估治疗效果。我们的模型是参数混合模型到具有事件发生时间结局的半参数设置的自然扩展。我们提出了一种非参数最大似然估计方法进行推断，并建立了相关的渐近性质。我们的方法通过一项针对恶性胶质瘤患者的可生物降解聚合物递送化疗的随机临床试验进行了说明。
