Lin Huazhen, Zhou Ling, Li Chunhong, Li Yi
Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan, China.
Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
Biometrics. 2014 Sep;70(3):599-607. doi: 10.1111/biom.12178. Epub 2014 Apr 21.
Semicompeting risk outcome data (e.g., time to disease progression and time to death) are commonly collected in clinical trials. However, analysis of these data is often hampered by a scarcity of available statistical tools. As such, we propose a novel semiparametric transformation model that improves the existing models in the following two ways. First, it estimates regression coefficients and association parameters simultaneously. Second, the measure of surrogacy, for example, the proportion of the treatment effect that is mediated by the surrogate and the ratio of the overall treatment effect on the true endpoint over that on the surrogate endpoint, can be directly obtained. We propose an estimation procedure for inference and show that the proposed estimator is consistent and asymptotically normal. Extensive simulations demonstrate the valid usage of our method. We apply the method to a multiple myeloma trial to study the impact of several biomarkers on patients' semicompeting outcomes--namely, time to progression and time to death.
半竞争风险结局数据（例如疾病进展时间和死亡时间）在临床试验中经常被收集。然而，这些数据的分析常常因可用统计工具的匮乏而受阻。因此，我们提出了一种新颖的半参数转换模型，该模型在以下两个方面改进了现有模型。首先，它同时估计回归系数和关联参数。其次，可以直接获得替代指标，例如由替代指标介导的治疗效果比例以及总体治疗效果对真实终点与对替代终点的比率。我们提出了一种用于推断的估计程序，并表明所提出的估计量是一致的且渐近正态。大量模拟证明了我们方法的有效使用。我们将该方法应用于一项多发性骨髓瘤试验，以研究几种生物标志物对患者半竞争结局（即进展时间和死亡时间）的影响。
