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Bayesian estimation and model selection in group-based trajectory models.

Emma Zang1, Justin T Max2

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We introduce a Bayesian group-based trajectory model (GBTM) for analyzing complex developmental patterns. This advanced method simplifies model selection, improving accuracy for longitudinal data analysis.

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Area of Science:

  • Statistics
  • Biostatistics
  • Psychometrics

Background:

  • Group-based trajectory modeling (GBTM) identifies distinct developmental pathways.
  • Traditional GBTMs require complex model selection, often involving polynomial functions and extensive searches.
  • Handling uncertainty in model selection remains a challenge in longitudinal data analysis.

Purpose of the Study:

  • To develop an enhanced Bayesian group-based trajectory model (GBTM).
  • To incorporate dual trajectories and Bayesian model averaging for robust model selection.
  • To address the complexity and computational burden of traditional GBTM model selection.

Main Methods:

  • A novel Bayesian group-based trajectory model (GBTM) framework was developed.
  • The model integrates dual trajectories and Bayesian model averaging for enhanced model selection.
  • The framework supports various standard distributions, including normal, censored normal, binary, and ordered outcomes.

Main Results:

  • The proposed Bayesian GBTM simplifies model selection by requiring only one model fit.
  • The approach effectively accounts for uncertainty in model selection, unlike brute-force methods.
  • The framework accommodates diverse functional relationships within latent groups, including polynomials and covariates.

Conclusions:

  • The developed Bayesian GBTM offers a more efficient and robust approach to longitudinal data analysis.
  • This method reduces the computational complexity associated with selecting the best-fitting trajectory models.
  • The framework provides a unified approach to model selection, enhancing the reliability of identified developmental trajectories.