The difference in restricted mean survival time (RMST) is a clinically meaningful measure to quantify treatment effect in randomized controlled trials, especially when the proportional hazards assumption does not hold. Several frequentist methods exist to estimate RMST adjusted for covariates based on modeling and integrating the survival function. A more natural approach may be a regression model on RMST using pseudo-observations, which allows for a direct estimation without modeling the survival function. Only a few Bayesian methods exist, and each requires a model of the survival function. We developed a new Bayesian method that combines the use of pseudo-observations with the generalized method of moments. This offers RMST estimation adjusted for covariates without the need to model the survival function, making it more attractive than existing Bayesian methods. A simulation study was conducted with different time-dependent treatment effects (early, delayed, and crossing survival) and covariate effects, showing that our approach provides valid results, aligns with existing methods, and shows improved precision after covariate adjustment. For illustration, we applied our approach to a phase III trial in prostate cancer, providing estimates of the treatment effect on RMST, comparable to existing methods. In addition, our approach provided the effect of other covariates on RMST and determined the posterior probability of the difference in RMST exceeds any given time threshold for any covariate, allowing for nuanced and interpretable results.

翻译：限制平均生存时间（RMST）差异是量化随机对照试验中治疗效果的一种具有临床意义的指标，尤其在比例风险假设不成立时。现有多种频率学方法基于建模和积分生存函数来估计校正协变量后的RMST。一种更自然的方法可能是使用伪观测值对RMST建立回归模型，该方法无需对生存函数建模即可直接进行估计。目前贝叶斯方法较少，且均需建立生存函数模型。我们开发了一种新的贝叶斯方法，将伪观测值与广义矩估计法相结合。该方法无需对生存函数建模即可实现校正协变量的RMST估计，相比现有贝叶斯方法更具优势。通过模拟研究，我们考察了不同时间依赖性治疗效果（早期、延迟及交叉生存）和协变量效应，结果表明：我们的方法能提供有效结果，与现有方法结果一致，且在协变量校正后显示出更高的精确度。为说明方法的应用，我们将其应用于一项前列腺癌III期试验，获得了与现有方法可比的RMST治疗效果估计值。此外，我们的方法还能提供其他协变量对RMST的影响，并确定任意协变量对应的RMST差异超过给定时间阈值的后验概率，从而获得更细致且可解释的结果。