"Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event Data"

TL;DR

This paper introduces a novel spatial joint modeling approach for longitudinal and time-to-event data using Bayesian structured piece-wise additive models, improving estimation accuracy and computational efficiency, especially for spatial effects.

Contribution

It presents a new Bayesian structured piece-wise additive joint model with spatial effects, addressing computational challenges and enhancing estimation in longitudinal and survival data.

Findings

Good estimation of spatial effects demonstrated in simulations

Model is twice as fast as benchmark approaches

Performs well with imbalanced data and few events

Abstract

Joint models for longitudinal and time-to-event data have seen many developments in recent years. Though spatial joint models are still rare and the traditional proportional hazards formulation of the time-to-event part of the model is accompanied by computational challenges. We propose a joint model with a piece-wise exponential formulation of the hazard using the counting process representation of a hazard and structured additive predictors able to estimate (non-)linear, spatial and random effects. Its capabilities are assessed in a simulation study comparing our approach to an established one and highlighted by an example on physical functioning after cardiovascular events from the German Ageing Survey. The Structured Piecewise Additive Joint Model yielded good estimation performance, also and especially in spatial effects, while being double as fast as the chosen benchmark approach and performing stable in imbalanced data setting with few events.