Withdrawn: 08 Bayesian Recursive Copula Survival Models with Functional Covariates

Thomas M. Menino Convention & Exhibition Center 

Causal inference for time-to-event outcomes faces challenges from endogenous treatments and high-frequency functional covariates. While recursive copula models address endogeneity via latent dependence, existing methods struggle to jointly model binary exposures and survival endpoints with high-dimensional functional data. We propose a Deep Probabilistic Recursive Copula Model integrating variational autoencoders (VAEs) with recursive copula-based survival modeling. Our framework learns nonlinear representations of functional profiles via VAEs to parameterize the copula structure, enabling scalable modeling of complex exposure–outcome dependence. By employing variational inference, we approximate the posterior via evidence lower bound maximization, avoiding computationally intensive MCMC. Simulations show our approach yields lower bias than two-stage estimators and significant computational gains over MCMC-based Bayesian models. We apply our approach to REGARDS and NHANES 2012-2024 to estimate the causal effect of chronic disease status on survival adjusting for physical activity behavior. 

Recursive Copula

Variational Inference

Functional Data Analysis

Survival Analysis

Endogeneity

Deep Learning 

Biometrics Section