A joint spatiotemporal model for multiple longitudinal markers and competing events

TL;DR

This paper introduces a novel joint spatiotemporal model for multiple longitudinal markers and competing events, addressing the challenge of undefined disease onset in chronic diseases by capturing shared latent processes.

Contribution

It proposes a new model that captures disease progression without a clear onset, using a shared latent age and spatial ordering of outcomes, validated on simulated and real ALS data.

Findings

Model performs comparably to existing joint models.

Captures latent disease age and outcome ordering effects.

Provides interpretable insights into disease progression.

Abstract

Non-terminal events can represent a meaningful change in a patient's life. Thus, better understanding and predicting their occurrence can bring valuable information to individuals. In a context where longitudinal markers could inform these events, joint models with competing risks have been developed. Their precision relies on a reference time for which disease onset is often used. Nevertheless, chronic diseases have no clear onset, making it difficult to define a precise reference time. We propose a Joint cause-specific Spatiotemporal model to overcome this limitation and to capture a shared latent process, a latent age (temporal aspect), associated with the ordering of the longitudinal outcomes (spatial aspect). First, we validated our model on simulated real-like data. Then, we benchmarked our model with a shared-random-effect joint model on real ALS data using the PRO-ACT dataset. Finally, to show how the model could be used for description tasks, we analysed the impact of sex and onset site on the progression of ALS as well as the initiation of Non-Invasive Ventilation. The Joint cause-specific spatiotemporal model achieved similar performance to the shared random effect joint model while capturing the latent disease age and the impact of the ordering of longitudinal outcomes on the occurrence of the events with fewer parameters. The application study confirmed existing results for the Longitudinal outcomes and showed how to interpret the model. The proposed approach by disentangling a temporal and a spatial aspect of the disease opens the perspective to capture meaningful change in future clinical trials.