🤖 AI Summary
Traditional accelerated failure time (AFT) models assume constant covariate effects across all survival quantiles, limiting their ability to capture effect heterogeneity. To address this, we propose an interpretable, quantile-dependent multiplicative effects framework—the first to derive closed-form analytic expressions for covariate effects on the quantile scale within the AFT paradigm. Integrating the g-formula, our approach enables standardized estimation of both conditional and marginal effects under left truncation and arbitrary censoring mechanisms. The method combines Bayesian inference, flexible nonlinear functional forms, and quantile-specific posterior inference. Evaluated in an Alzheimer’s disease cohort, it robustly uncovers distinct influences of age, APOE status, and other covariates on early versus late survival quantiles—revealing previously undetected effect heterogeneity. This enhances both statistical precision and clinical interpretability of survival effect estimates.
📝 Abstract
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.