02: Fast Variational Bayesian Inference for Correlated Survival Data: An Application to Invasive Mechanical Ventilation Duration Analysis

Music City Center 

Correlated survival data are prevalent in various clinical settings and have been extensively discussed in the literature. One of the most common types of correlated survival data is clustered survival data, where the survival times within a cluster are correlated. Our study is motivated by invasive mechanical ventilation data from different intensive care units (ICUs) in Ontario, Canada, forming multiple clusters. To account for correlation within clusters, we introduce a shared frailty log-logistic accelerated failure time model with a random intercept specific to each cluster. We present a novel, fast variational Bayes (VB) algorithm for parameter inference and evaluate its performance using simulation studies that vary the number of clusters and their sizes. We also compare the performance of our proposed VB algorithm with the h-likelihood method and a Markov Chain Monte Carlo (MCMC) algorithm. The proposed algorithm delivers satisfactory results and demonstrates computational efficiency over the MCMC algorithm. We apply our method to the ICU ventilation data from Ontario to investigate the random effect of ICU site on ventilation duration.