A Bayesian Functional Accelerated Failure-Time Model with Varying Effects Correcting for Measurement Error
校正测量误差的变效应贝叶斯函数型加速失效时间模型AI 解读 ↓
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校正测量误差的变效应贝叶斯函数型加速失效时间模型
- 研究问题
- 如何估计函数型暴露与事件时间的关系，使其随时间及人群特征非线性变化，同时校正连续轨迹中的测量误差？
- 方法与思路
- 作者以高斯过程单指标结构建模变效应函数，并结合代理观测与工具变量处理潜在函数型暴露的测量误差。
- 材料陈述的贡献
- 摘要称模拟能恢复变效应并降低忽略测量误差造成的偏差，且将模型用于 REGARDS 中步数与卒中死亡时间的关系分析。
- 阅读关注点
- 应核对代理与工具变量的有效性条件、潜在暴露识别、函数型效应中时间的含义，以及删失处理与人群异质性设定。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明工具变量条件及识别细节，不能据此作因果解释。
原文摘要
Functional data collected as continuously observed trajectories arise naturally in many biomedical settings, and a key inferential goal is understanding how such functional covariates relate to time-to-event outcomes while allowing that relationship to vary across subgroups defined by scalar characteristics. Existing frequentist approaches to functional accelerated failure-time (AFT) models struggle to flexibly capture the joint, nonlinear influence of scalar covariates and time on the functional effect, and none adequately address the measurement error that frequently contaminates functionally observed exposures. We propose a Bayesian functional AFT model in which the functional coefficient is a varying effect function of both time and a set of scalar covariates, modeled through a Gaussian process single-index structure that provides a flexible, nonlinear framework for subgroup modification. Measurement error in the functional covariate is handled by pairing a proxy observation with an instrumental variable that accommodates non-linear associations with the latent functional exposure. A prominent source of such functional data is wearable devices, which can continuously monitor physical activity (PA) behavioral patterns over time, yet whose outputs are well known to be prone to measurement error and to exhibit heterogeneous associations with health outcomes across demographic subgroups. Through simulations, we show that our approach recovers the true varying functional effects and reduces bias relative tonaïve models that ignore measurement error. We apply our methods to the Reasons for Geographical and Racial Differences in Stroke (REGARDS) study to investigate how step-count physical activity relates to time-to-death from ischemic stroke across racial and regional groups.