Scalable Joint Modeling of Multiple Biomarkers and Survival Outcomes for Massive Biobank Data

Music City Center 

Despite the explosive growth of literature on joint models to correlate longitudinal and time-to-event data, efficient implementation of jointly modeling multiple biomarkers and time-to-event outcome has lagged behind, and their current implementations do not scale to large datasets with tens of thousands to millions of subjects. To address this, we propose a fast approximate expectation-maximization (EM) algorithm for a semiparametric joint model that handles multiple biomarkers and competing risks time-to-event outcome. The fast approximate EM algorithm utilizes both customized linear scan algorithms and a normal approximation of the posterior distribution of random effects, significantly reducing the computational burdens by a factor of up to hundreds of thousands compared to the existing approaches, often reducing the runtime from days to minutes. We validate the accuracy and efficiency of our approximation method through various simulation studies and further demonstrate its practical applications by using a real world large-scale Biobank study.

competing risks

massive data

multiple biomarkers

normal approximation

scalable joint models 

Biometrics Section