Abstract：
Analysis of the restricted mean survival time (RMST) has become increasingly common in biomedical studies during the last decade as a means of estimating treatment or covariate effects on survival. Advantages of RMST over the hazard ratio (HR) include increased interpretability and lack of reliance on the often tenuous proportional hazards assumption. Some authors have argued that RMST regression should generally be the frontline analysis as opposed to methods based on counting process increments. However, in order for the use of the RMST to be more mainstream, it is necessary to broaden the range of data structures to which pertinent methods can be applied. In this report, we address this issue from two angles. First, most of existing methodological development for directly modeling RMST has focused on multiplicative models. An additive model may be preferred due to goodness of fit and/or parameter interpretation. Second, many settings encountered nowadays feature high-dimensional categorical (nuisance) covariates, for which parameter estimation is best avoided. Motivated by these considerations, we propose stratified additive models for direct RMST analysis. The proposed methods feature additive covariate effects. Moreover, nuisance factors can be factored out of the estimation, akin to stratification in Cox regression, such that focus can be appropriately awarded to the parameters of chief interest. Large-sample properties of the proposed estimators are derived, and a simulation study is performed to assess finite-sample performance. In addition, we provide techniques for evaluating a fitted model with respect to risk discrimination and predictive accuracy. The proposed methods are then applied to liver transplant data to estimate the effects of donor characteristics on posttransplant survival time.
摘要：
在过去的十年中，限制平均生存时间（RMST）的分析在生物医学研究中变得越来越普遍，作为评估治疗或对生存的协变量影响的手段。RMST相对于危险比（HR）的优势包括增加的可解释性和对通常脆弱的比例危险假设的依赖。一些作者认为RMST回归通常应该是前线分析，而不是基于计数过程增量的方法。然而,为了使RMST的使用更加主流，有必要扩大可以应用相关方法的数据结构的范围。在这份报告中,我们从两个角度来解决这个问题。首先,直接建模RMST的大多数现有方法发展都集中在乘法模型上。由于拟合和/或参数解释的良好性,加法模型可能是优选的。第二,现在遇到的许多设置都具有高维分类(令人讨厌)协变量，最好避免参数估计。出于这些考虑，我们提出了用于直接RMST分析的分层加性模型。所提出的方法具有加性协变量效应。此外，干扰因素可以从估计中考虑，类似于Cox回归中的分层，这样可以将重点适当地授予主要兴趣的参数。推导了所提出的估计量的大样本性质，并进行了仿真研究以评估有限样本的性能。此外,我们提供了在风险区分度和预测准确性方面评估拟合模型的技术。然后将提出的方法应用于肝移植数据，以估计供体特征对移植后存活时间的影响。
