Group-based multi-trajectory modeling

TLDR

Identifying and monitoring multiple disease biomarkers and other clinically important factors is of great clinical relevance, yet conventional statistical practice often fails to fully exploit multivariate longitudinal data for tracking disease trajectories, and group‑based trajectory modeling has been used to identify clusters following similar single‑indicator trajectories. The authors demonstrate a method called multi‑trajectory modeling that is designed to overcome this limitation. Multi‑trajectory modeling identifies latent clusters of individuals following similar trajectories across multiple indicators of an outcome of interest and is based on a finite mixture likelihood that we derive and illustrate with two examples.

Abstract

Identifying and monitoring multiple disease biomarkers and other clinically important factors affecting the course of a disease, behavior or health status is of great clinical relevance. Yet conventional statistical practice generally falls far short of taking full advantage of the information available in multivariate longitudinal data for tracking the course of the outcome of interest. We demonstrate a method called multi-trajectory modeling that is designed to overcome this limitation. The method is a generalization of group-based trajectory modeling. Group-based trajectory modeling is designed to identify clusters of individuals who are following similar trajectories of a single indicator of interest such as post-operative fever or body mass index. Multi-trajectory modeling identifies latent clusters of individuals following similar trajectories across multiple indicators of an outcome of interest (e.g., the health status of chronic kidney disease patients as measured by their eGFR, hemoglobin, blood CO2 levels). Multi-trajectory modeling is an application of finite mixture modeling. We lay out the underlying likelihood function of the multi-trajectory model and demonstrate its use with two examples.