# Joint Latent Class Trees: A Tree-Based Approach to Modeling Time-to-event and Longitudinal Data

## TL;DR

This paper introduces JLCT, a fast, semiparametric tree-based method for modeling joint longitudinal and time-to-event data, effectively incorporating time-varying covariates and outperforming traditional parametric models.

## Contribution

The paper presents JLCT, a novel tree-based joint modeling approach that is computationally efficient and capable of using time-varying covariates, unlike existing parametric methods.

## Key findings

- JLCT outperforms JLCM in prediction accuracy.
- JLCT demonstrates significant speed improvements.
- JLCT effectively incorporates time-varying covariates.

## Abstract

In this paper, we propose a semiparametric, tree based joint latent class modeling approach (JLCT) to model the joint behavior of longitudinal and time-to-event data. Existing joint latent class modeling approaches are parametric and can suffer from high computational cost. The most common parametric approach, the joint latent class model (JLCM), further restricts analysis to using time-invariant covariates in modeling survival risks and latent class memberships. Instead, the proposed JLCT is fast to fit, and can use time-varying covariates in all of its modeling components. We demonstrate the prognostic value of using time-varying covariates, and therefore the advantage of JLCT over JLCM on simulated data. We further apply JLCT to the PAQUID data set and confirm its superior prediction performance and orders-of-magnitude speedup over JLCM.