Characterizing quantile-varying covariate effects under the accelerated failure time model.
Characterizing quantile-varying covariate effects under the accelerated failure time model.
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DOI：
10.1093/biostatistics/kxac052
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发表时间：
2023-01
期刊：
影响因子：
2.1
通讯作者：
Harrison T. Reeder;Kyu Ha Lee;S. Haneuse
中科院分区：
文献类型：
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作者：
Harrison T. Reeder;Kyu Ha Lee;S. Haneuse
生存分析中的一项重要任务是选择相关协变量与事件时间结果之间关系的结构。例如，加速失效时间（AFT）模型将每个协变量效应结构为所有生存分位数的结果分布中的常数乘法移位。虽然这种结构很简单，但它不能检测或捕捉分布中不同分位数的影响，这种限制类似于Cox模型中只允许比例风险。为了解决这个问题，我们在AFT模型下提出了一个分位数变化乘法效应的一般框架。具体来说，我们在AFT模型中嵌入了灵活的回归结构，并推导出了一个新的公式，用于分位数尺度上的可解释效应。提出了一种基于g公式的回归标准化方案，以便对感兴趣的暴露进行协变量条件效应和边际效应的估计。我们实现了一种用户友好的贝叶斯方法来估计和量化不确定性，同时考虑到左截断和复杂的审查。我们强调通过数值和图形工具对该模型的直观解释，并通过模拟和应用于阿尔茨海默病和痴呆症的研究来说明其性能。
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.