An important task in health research is to characterize time-to-event outcomes such as disease onset or mortality in terms of a potentially high-dimensional set of risk factors. For example, prospective cohort studies of Alzheimer's disease typically enroll older adults for observation over several decades to assess the long-term impact of genetic and other factors on cognitive decline and mortality. The accelerated failure time model is particularly well-suited to such studies, structuring covariate effects as `horizontal' changes to the survival quantiles that conceptually reflect shifts in the outcome distribution due to lifelong exposures. However, this modeling task is complicated by the enrollment of adults at differing ages, and intermittent followup visits leading to interval censored outcome information. Moreover, genetic and clinical risk factors are not only high-dimensional, but characterized by underlying grouping structure, such as by function or gene location. Such grouped high-dimensional covariates require shrinkage methods that directly acknowledge this structure to facilitate variable selection and estimation. In this paper, we address these considerations directly by proposing a Bayesian accelerated failure time model with a group-structured lasso penalty, designed for left-truncated and interval-censored time-to-event data. We develop a custom Markov chain Monte Carlo sampler for efficient estimation, and investigate the impact of various methods of penalty tuning and thresholding for variable selection. We present a simulation study examining the performance of this method relative to models with an ordinary lasso penalty, and apply the proposed method to identify groups of predictive genetic and clinical risk factors for Alzheimer's disease in the Religious Orders Study and Memory and Aging Project (ROSMAP) prospective cohort studies of AD and dementia.

翻译：健康研究中的一项重要任务是描述诸如疾病发作或死亡率等时间至事件结局与潜在高维风险因素集之间的关系。例如，阿尔茨海默病的前瞻性队列研究通常招募老年人进行长达数十年的观察，以评估遗传及其他因素对认知衰退和死亡率的长期影响。加速失效时间模型特别适用于此类研究，它通过将协变量效应构建为生存分位数的“水平”变化，概念上反映了终身暴露导致的结局分布偏移。然而，该建模任务因招募不同年龄的成年人而复杂化，且间歇性随访导致结局信息出现区间删失。此外，遗传与临床风险因素不仅具有高维特征，还呈现出内在的分组结构（如按功能或基因位置分组）。此类分组高维协变量需要直接承认这种结构的收缩方法，以促进变量选择和估计。本文直接针对这些考量，提出一种带有分组结构套索惩罚的贝叶斯加速失效时间模型，专为左截断与区间删失的时间至事件数据设计。我们开发了定制化的马尔可夫链蒙特卡洛采样器以实现高效估计，并探讨了惩罚调优与阈值化方法对变量选择的影响。通过模拟研究比较该方法与普通套索惩罚模型的性能，并将所提方法应用于识别宗教秩序研究与记忆和衰老项目（ROSMAP）中阿尔茨海默病及痴呆前瞻性队列研究中的预测性遗传与临床风险因素组。