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Group-based trajectory modeling in clinical research.

Daniel S Nagin¹, Candice L Odgers

¹Heinz School of Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3890, USA. dn03@andrew.cmu.edu

Annual Review of Clinical Psychology

|March 3, 2010

Summary

This summary is machine-generated.

Group-based trajectory models help map symptom development and treatment response heterogeneity in clinical research. This review offers an overview, applications, challenges, and guidelines for using these statistical models.

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Area of Science:

Biostatistics
Clinical Research Methodology
Longitudinal Data Analysis

Background:

Group-based trajectory models (GBTMs) are emerging statistical tools in clinical research.
These models are used to understand symptom progression and individual differences in treatment outcomes.
Growth mixture modeling (GMM) is a related technique for identifying distinct developmental patterns.

Purpose of the Study:

To provide a nontechnical overview of GBTMs and GMM.
To showcase diverse applications of these models in clinical research.
To discuss challenges and offer reporting guidelines for applied researchers.

Main Methods:

Review of existing literature on group-based modeling applications.
Nontechnical explanation of trajectory and mixture modeling concepts.
Synthesis of challenges and best practices in model reporting.

Main Results:

GBTMs and GMMs are versatile for analyzing longitudinal clinical data.
Key challenges include model selection, interpretation, and reporting transparency.
Preliminary guidelines are proposed to enhance the rigor of applied research.

Conclusions:

Group-based modeling offers valuable insights into clinical trajectories and treatment heterogeneity.
Standardized reporting is crucial for the reliable application of these advanced statistical methods.
Future research should explore causal inference applications of trajectory models.