Crouch Luis Alexander, Zheng Cheng, Chen Ying Qing
Department of Biostatistics, University of Washington, Seattle, Washington 98105, U.S.A.
Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, 53205, U.S.A.
Stat Biosci. 2017 Jun;9(1):298-315. doi: 10.1007/s12561-016-9180-x. Epub 2016 Oct 28.
For randomized clinical trials where the endpoint of interest is a time-to-event subject to censoring, estimating the treatment effect has mostly focused on the hazard ratio from the Cox proportional hazards model. Since the model's proportional hazards assumption is not always satisfied, a useful alternative, the so-called additive hazards model, may instead be used to estimate a treatment effect on the difference of hazard functions. Still, the hazards difference may be difficult to grasp intuitively, particularly in a clinical setting of, e.g., patient counseling, or resource planning. In this paper, we study the quantiles of a covariate's conditional survival function in the additive hazards model. Specifically, we estimate the residual time quantiles, i.e., the quantiles of survival times remaining at a given time , conditional on the survival times greater than , for a specific covariate in the additive hazards model. We use the estimates to translates the hazards difference into the difference in residual time quantiles, which allows a more direct clinical interpretation. We determine the asymptotic properties, assess the performance via Monte-Carlo simulations, and demonstrate the use of residual time quantiles in two real randomized clinical trials.
对于感兴趣的终点为受删失影响的事件发生时间的随机临床试验，估计治疗效果大多集中在Cox比例风险模型的风险比上。由于该模型的比例风险假设并非总是成立，一种有用的替代方法，即所谓的相加风险模型，可用于估计治疗对风险函数差异的影响。然而，风险差异可能难以直观理解，特别是在例如患者咨询或资源规划等临床环境中。在本文中，我们研究相加风险模型中协变量条件生存函数的分位数。具体而言，我们估计剩余时间分位数，即在相加风险模型中，对于特定协变量，在生存时间大于给定时间的条件下，给定时间剩余生存时间的分位数。我们使用这些估计值将风险差异转化为剩余时间分位数的差异，这使得临床解释更加直接。我们确定渐近性质，通过蒙特卡罗模拟评估性能，并在两项实际随机临床试验中展示剩余时间分位数的应用。
