Reeder Harrison T, Lee Kyu Ha, Haneuse Sebastien
Biostatistics, Massachusetts General Hospital, 50 Staniford Street, Suite 560, Boston, MA 02114, USA and Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
Departments of Nutrition, Epidemiology, and Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA.
Biostatistics. 2024 Apr 15;25(2):449-467. doi: 10.1093/biostatistics/kxac052.
An important task in survival analysis is choosing a structure for the relationship between covariates of interest and the time-to-event outcome. For example, the accelerated failure time (AFT) model structures each covariate effect as a constant multiplicative shift in the outcome distribution across all survival quantiles. Though parsimonious, this structure cannot detect or capture effects that differ across quantiles of the distribution, a limitation that is analogous to only permitting proportional hazards in the Cox model. To address this, we propose a general framework for quantile-varying multiplicative effects under the AFT model. Specifically, we embed flexible regression structures within the AFT model and derive a novel formula for interpretable effects on the quantile scale. A regression standardization scheme based on the g-formula is proposed to enable the estimation of both covariate-conditional and marginal effects for an exposure of interest. We implement a user-friendly Bayesian approach for the estimation and quantification of uncertainty while accounting for left truncation and complex censoring. We emphasize the intuitive interpretation of this model through numerical and graphical tools and illustrate its performance through simulation and application to a study of Alzheimer's disease and dementia.
生存分析中的一项重要任务是为感兴趣的协变量与事件发生时间结果之间的关系选择一种结构。例如，加速失效时间（AFT）模型将每个协变量效应构建为所有生存分位数上结果分布的恒定乘法偏移。尽管这种结构简洁，但它无法检测或捕捉分布分位数之间不同的效应，这一局限性类似于Cox模型中仅允许比例风险。为了解决这个问题，我们提出了一个在AFT模型下用于分位数变化乘法效应的通用框架。具体而言，我们将灵活的回归结构嵌入到AFT模型中，并推导出一个关于分位数尺度上可解释效应的新公式。提出了一种基于g公式的回归标准化方案，以能够估计感兴趣暴露的协变量条件效应和边际效应。我们实现了一种用户友好的贝叶斯方法来进行估计和不确定性量化，同时考虑左截断和复杂删失。我们通过数值和图形工具强调该模型的直观解释，并通过模拟以及应用于阿尔茨海默病和痴呆症研究来说明其性能。
