Flexible Bayesian additive joint models with an application to type 1 diabetes research

TL;DR

This paper introduces a flexible Bayesian additive joint modeling framework that captures complex nonlinear and time-varying relationships between biomarkers and disease progression, demonstrated through type 1 diabetes research.

Contribution

It develops a comprehensive Bayesian additive joint model allowing for diverse effects and nonlinear trajectories, advancing analysis of biomarker-disease associations.

Findings

Captured highly nonlinear subject-specific marker trajectories

Modeled time-varying association between biomarkers and disease progression

Implemented in R-package bamlss for practical use

Abstract

The joint modeling of longitudinal and time-to-event data is an important tool of growing popularity to gain insights into the association between a biomarker and an event process. We develop a general framework of flexible additive joint models that allows the specification of a variety of effects, such as smooth nonlinear, time-varying and random effects, in the longitudinal and survival parts of the models. Our extensions are motivated by the investigation of the relationship between fluctuating disease-specific markers, in this case autoantibodies, and the progression to the autoimmune disease type 1 diabetes. By making use of Bayesian P-splines we are in particular able to capture highly nonlinear subject-specific marker trajectories as well as a time-varying association between the marker and the event process allowing new insights into disease progression. The model is estimated within a Bayesian framework and implemented in the R-package bamlss.