Estimation of treatment effects in weighted log-rank tests.
Estimation of treatment effects in weighted log-rank tests.
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DOI：
10.1016/j.conctc.2017.09.004
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发表时间：
2017-12
影响因子：
1.5
通讯作者：
León LF
中科院分区：
文献类型：
--
作者：
Lin RS;León LF
临床试验中已观察到不成比例的危险。对数秩检验失去了效力，并且标准 Cox 模型在这种情况下通常会产生有偏差的估计。采用加权对数秩检验来提高检验功效；然而，如何从临床效果角度解释测试结果并不直观。我们提出了一种基于 Cox 模型的时变治疗效果估计，以补充加权对数秩检验。该模型的评分检验相当于加权对数秩检验，并且可以通过拟合时变协变量 Cox 模型来获得治疗效果的时间曲线。仿真结果表明，所提出的模型保留了 I 类误差，并且在非比例危险场景下比对数秩检验获得了更高的功效。标准 Cox 模型会产生有偏效应估计，而如果正确指定权重函数，则所提出的模型会产生无偏估计。与标准 Cox 模型相比，它还实现了更好的模型拟合和增强的灵活性，以适应非比例风险。所提出的方法使加权对数秩检验的假设变得明确，并且可以基于先验知识或模型拟合优度来评估假设的有效性。它还有助于将加权对数秩检验结果转化为具有直观解释的治疗效果的定量估计。所提出的方法可以定期进行，以补充加权对数秩检验，特别是在预期存在不成比例风险的情况下。
Non-proportional hazards have been observed in clinical trials. The log-rank test loses power and the standard Cox model generally produces biased estimates under such conditions. Weighted log-rank tests have been utilized to increase the test power; however, it is not intuitive how to interpret the test result in terms of the clinical effect. We propose a Cox-model based time-varying treatment effect estimate to complement the weighted log-rank test. The score test from the proposed model is equivalent to the weighted log-rank test, and a time-profile of the treatment effect can be obtained by fitting a time-varying covariate Cox model. Simulation results show that the proposed model preserves type-I error and achieve higher power than log-rank tests under non-proportional hazards scenarios. Whereas the standard Cox model produces biased effect estimates, the proposed model produces unbiased estimates if the weight function is correctly specified. It also achieves a better model fit and an enhanced flexibility to accommodate non-proportional hazards compared to the standard Cox model. The proposed approach makes the assumptions of the weighted log-rank test explicit and the validity of assumptions can be assessed based on prior knowledge or model goodness-of-fit. It also helps to translate the weighted log-rank test results into quantitative estimates of the treatment effect with intuitive interpretation. The proposed method can be routinely conducted to complement weighted log-rank tests, especially in the setting where non-proportional hazards are expected.