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应的临床意义指标，尤其在比例风险假设不成立时更为适用。现有多种频率学方法可通过建模和积分生存函数来估计经协变量调整的RMST。一种更自然的方法是使用伪观测值建立RMST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获得经协变量调整的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应（早期效应、延迟效应及交叉生存）和协变量效应，结果表明：本方法能提供有效估计结果，与现有方法结论一致，且在协变量调整后精度有所提升。为示例说明，我们将本方法应用于前列腺癌III期临床试验，获得了与现有方法可比的治疗效应RMST估计值。此外，本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，从而获得更精细且可解释的结果。