Area between trajectories: Insights into optimal group selection and trajectory heterogeneity in group-based trajectory modeling

TL;DR

This paper introduces the Area Between Trajectories (ABTs) as a new method to quantify heterogeneity in group-based trajectory modeling, aiding in optimal group selection and clinical relevance assessment.

Contribution

The paper presents ABTs as a novel metric for evaluating trajectory differences, addressing the gap between statistical criteria and clinical interpretability in GBTM.

Findings

ABTs effectively quantifies trajectory heterogeneity.

ABTs highlights limitations and potential uses in GBTM.

Application to simulated sleep data demonstrates practical utility.

Abstract

Group-based trajectory modeling (GBTM) is commonly used to identify longitudinal patterns in health outcomes among older adults, with determining the optimal number of groups being a crucial step. While statistically grounded criteria are primarily relied upon, clinical relevance is gradually emphasized in medicine to ensure that the identified trajectory heterogeneity appropriately reflects changes in a disease or symptom over time. However, such considerations are often judged through visual comparisons, without concrete approaches for their application. To address this, the Area Between Trajectories (ABTs) was introduced as insights for quantifying trajectory group differences. Using a simulated sleep quality dataset, GBTM was applied to build and compare models. Subsequently, ABTs was demonstrated to show how it works, while also highlighting its limitations and potential applications.