A class of proportional win-fractions regression models for composite outcomes.
A class of proportional win-fractions regression models for composite outcomes.
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作者：
Mao L;Wang T
胜率作为一种简单直观的方法来分析临床试验中的优先复合终点，越来越受到人们的关注。为了将其从两个样本比较扩展到回归，我们提出了一类新颖的半参数模型，其中包括两个样本胜率和到第一个事件的时间上的传统 Cox 比例风险模型作为特殊情况。假设协变量特定的获胜和损失分数与时间成正比，则回归系数与审查分布无关，并且可以解释为协变量增加一个单位所产生的对数获胜比率。构造 U 统计估计函数，其形式是通过成对残差过程集成的任意协变量特定权重过程，以获得回归参数的一致估计量。估计量的渐近性质是使用 U 过程的一致弱收敛理论导出的。对“评分”过程的目视检查为比例假设的合理性提供了有用的线索。使用来自主要心血管试验的模拟和真实数据进行的广泛数值研究表明，回归方法提供了对协变量效应的有效推断，并且在效率和鲁棒性方面优于双样本胜率。所提出的方法在 R 包 WR 中实现，可从综合 R 档案网络 (CRAN) 公开获取。
The win ratio is gaining traction as a simple and intuitive approach to analysis of prioritized composite endpoints in clinical trials. To extend it from two-sample comparison to regression, we propose a novel class of semiparametric models that includes as special cases both the two-sample win ratio and the traditional Cox proportional hazards model on time to the first event. Under the assumption that the covariate-specific win and loss fractions are proportional over time, the regression coefficient is unrelated to the censoring distribution and can be interpreted as the log win ratio resulting from one-unit increase in the covariate. U-statistic estimating functions, in the form of an arbitrary covariate-specific weight process integrated by a pairwise residual process, are constructed to obtain consistent estimators for the regression parameter. The asymptotic properties of the estimators are derived using uniform weak convergence theory for U-processes. Visual inspection of a “score” process provides useful clues as to the plausibility of the proportionality assumption. Extensive numerical studies using both simulated and real data from a major cardiovascular trial show that the regression methods provide valid inference on covariate effects and outperform the two-sample win ratio in both efficiency and robustness. The proposed methodology is implemented in the R-package WR, publicly available from the Comprehensive R Archive Network (CRAN).
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