Efficient Implementation of a Semiparametric Joint Model for Multivariate Longitudinal Biomarkers and Competing Risks Time-to-Event Data

TL;DR

This paper introduces an efficient, scalable implementation of a semiparametric multivariate joint model for large-scale biomedical data, significantly reducing computational resources needed for complex joint modeling tasks.

Contribution

The authors develop a novel EM-based approach with normal approximation and linear scan algorithms, enabling fast analysis of high-dimensional joint models on biobank-scale datasets.

Findings

Method reduces computation time and memory usage

Demonstrated scalability with simulation studies

Applied successfully to PBC dataset with five biomarkers

Abstract

Joint modeling has become increasingly popular for characterizing the association between one or more longitudinal biomarkers and competing risks time-to-event outcomes. However, semiparametric multivariate joint modeling for large-scale data encounter substantial statistical and computational challenges, primarily due to the high dimensionality of random effects and the complexity of estimating nonparametric baseline hazards. These challenges often lead to prolonged computation time and excessive memory usage, limiting the utility of joint modeling for biobank-scale datasets. In this article, we introduce an efficient implementation of a semiparametric multivariate joint model, supported by a normal approximation and customized linear scan algorithms within an expectation-maximization (EM) framework. Our method significantly reduces computation time and memory consumption, enabling the analysis of data from thousands of subjects. The scalability and estimation accuracy of our approach are demonstrated through two simulation studies. We also present an application to the Primary Biliary Cirrhosis (PBC) dataset involving five longitudinal biomarkers as an illustrative example. A user-friendly R package, \texttt{FastJM}, has been developed for the shared random effects joint model with efficient implementation. The package is publicly available on the Comprehensive R Archive Network: https://CRAN.R-project.org/package=FastJM.