Jiang Honghua, Kulkarni Pandurang M, Wang Yanping, Mallinckrodt Craig H
Eli Lilly and Company, Indianapolis, IN, USA.
Pharm Stat. 2016 Jan-Feb;15(1):46-53. doi: 10.1002/pst.1725. Epub 2015 Nov 26.
In randomized clinical trials with time-to-event outcomes, the hazard ratio is commonly used to quantify the treatment effect relative to a control. The Cox regression model is commonly used to adjust for relevant covariates to obtain more accurate estimates of the hazard ratio between treatment groups. However, it is well known that the treatment hazard ratio based on a covariate-adjusted Cox regression model is conditional on the specific covariates and differs from the unconditional hazard ratio that is an average across the population. Therefore, covariate-adjusted Cox models cannot be used when the unconditional inference is desired. In addition, the covariate-adjusted Cox model requires the relatively strong assumption of proportional hazards for each covariate. To overcome these challenges, a nonparametric randomization-based analysis of covariance method was proposed to estimate the covariate-adjusted hazard ratios for multivariate time-to-event outcomes. However, empirical evaluations of the performance (power and type I error rate) of the method have not been studied. Although the method is derived for multivariate situations, for most registration trials, the primary endpoint is a univariate outcome. Therefore, this approach is applied to univariate outcomes, and performance is evaluated through a simulation study in this paper. Stratified analysis is also investigated. As an illustration of the method, we also apply the covariate-adjusted and unadjusted analyses to an oncology trial.
在具有事件发生时间结局的随机临床试验中，风险比通常用于量化相对于对照的治疗效果。Cox回归模型通常用于调整相关协变量，以获得治疗组之间风险比的更准确估计。然而，众所周知，基于协变量调整的Cox回归模型的治疗风险比取决于特定的协变量，并且不同于总体平均的无条件风险比。因此，当需要进行无条件推断时，不能使用协变量调整的Cox模型。此外，协变量调整的Cox模型对每个协变量需要相对较强的比例风险假设。为了克服这些挑战，提出了一种基于非参数随机化的协方差分析方法，用于估计多变量事件发生时间结局的协变量调整风险比。然而，该方法性能（功效和I型错误率）的实证评估尚未得到研究。尽管该方法是针对多变量情况推导出来的，但对于大多数注册试验来说，主要终点是单变量结局。因此，本文将该方法应用于单变量结局，并通过模拟研究评估其性能。还研究了分层分析。作为该方法的一个示例，我们还将协变量调整和未调整分析应用于一项肿瘤学试验。
