Recent Advances in Group-Based Trajectory Modeling for Clinical Research

Area of Science:

• Clinical Psychology
• Biostatistics
• Longitudinal Data Analysis

Background:

• Group-based trajectory modeling (GBTM) is a powerful tool for analyzing longitudinal data.
• Its categorical nature is well-suited for clinical psychology and medicine, aiding in patient classification.
• However, several advanced capabilities remain underutilized in clinical research.

Purpose of the Study:

• To review recent advancements in GBTM.
• To highlight underappreciated capabilities of GBTM for clinical research.
• To provide guidance on selecting the optimal number of trajectory groups.

Main Methods:

• Review of recent literature on group-based trajectory modeling.
• Discussion of advanced GBTM features including handling attrition, joint and multi-trajectory models, beta distribution, and outcome prediction.
• Exploration of methods for determining the number of trajectory groups.

Main Results:

• GBTM offers advanced features like handling nonrandom attrition and joint/multitrajectory modeling.
• The beta distribution enhances modeling flexibility.
• GBTM can associate trajectories with future outcomes and estimate outcome probabilities.
• Methods for selecting the number of trajectory groups are discussed.

Conclusions:

• Recent advances significantly expand the utility of GBTM in clinical research.
• Underappreciated GBTM capabilities can improve patient classification and outcome prediction.
• Further adoption of these advanced methods will enhance clinical psychology and medicine.