We present a new estimator of the restricted mean survival time in randomized trials where there is right censoring that may depend on treatment and baseline variables. The proposed estimator leverages prognostic baseline variables to obtain equal or better asymptotic precision compared to traditional estimators. Under regularity conditions and random censoring within strata of treatment and baseline variables, the proposed estimator has the following features: (i) it is interpretable under violations of the proportional hazards assumption; (ii) it is consistent and at least as precise as the Kaplan–Meier and inverse probability weighted estimators, under identifiability conditions; (iii) it remains consistent under violations of independent censoring (unlike the Kaplan–Meier estimator) when either the censoring or survival distributions, conditional on covariates, are estimated consistently; and (iv) it achieves the nonparametric efficiency bound when both of these distributions are consistently estimated. We illustrate the performance of our method using simulations based on resampling data from a completed, phase 3 randomized clinical trial of a new surgical treatment for stroke; the proposed estimator achieves a 12% gain in relative efficiency compared to the Kaplan–Meier estimator. The proposed estimator has potential advantages over existing approaches for randomized trials with time-to-event outcomes, since existing methods either rely on model assumptions that are untenable in many applications, or lack some of the efficiency and consistency properties (i)–(iv). We focus on estimation of the restricted mean survival time, but our methods may be adapted to estimate any treatment effect measure defined as a smooth contrast between the survival curves for each study arm. We provide R code to implement the estimator.

中文翻译：

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无需假设成比例的风险，提高了具有生存结果的随机试验分析的准确性。

我们提出了一种新的估计值，用于随机试验中的受限平均生存时间，在这种情况下，正确的检查可能取决于治疗和基线变量。与传统估计器相比，拟议的估计器利用预后基线变量来获得相等或更好的渐近精度。在规律性条件下以及治疗和基线变量分层内的随机检查下，拟议的估算器具有以下特征：（i）在违反比例风险假设的前提下可以解释；（ii）在可识别性条件下，它是一致的，并且至少与Kaplan-Meier和逆概率加权估计器一样精确；（iii）在审查或生存分布情况下，在违反独立审查的情况下（与Kaplan-Meier估计器不同），它保持一致，以协变量为条件，一致估计；（iv）当一致估计这两个分布时，它将达到非参数效率范围。我们使用基于中风新手术治疗的已完成的3期随机临床试验的重采样数据进行模拟，说明了我们方法的性能。与Kaplan-Meier估计器相比，拟议的估计器相对效率提高了12％。拟议的估算器相对于具有达到事件发生时间结果的随机试验的现有方法具有潜在的优势，因为现有方法要么依赖于在许多应用中站不住脚的模型假设，要么缺乏某些效率和一致性属性（i）–（iv ）。我们专注于限制平均生存时间的估算，但是我们的方法可能适用于估计任何治疗效果量度，定义为每个研究组的生存曲线之间的平滑对比。我们提供R代码来实现估算器。