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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Recent Advances in Group-Based Trajectory Modeling for Clinical Research.

Daniel S Nagin¹, Bobby L Jones², Jonathan Elmer³

¹Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA;

Annual Review of Clinical Psychology

|February 21, 2024

Summary

This summary is machine-generated.

Group-based trajectory modeling (GBTM) helps identify patient groups with similar health journeys. Recent advances enhance its use in clinical research for better outcome prediction and patient classification.

Keywords:

finite mixture modeling joint trajectory modeling multitrajectory modeling prediction

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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.