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Does group-based trajectory modeling estimate spurious trajectories?

Miceline Mésidor1,2, Marie-Claude Rousseau1,2,3, Jennifer O'Loughlin1,2

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Group-based trajectory modelling (GBTM) can produce incorrect results. Researchers must use multiple criteria to assess model adequacy and avoid relying solely on average posterior probability to prevent spurious findings.

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Group-based trajectory modelling (GBTM) is a statistical technique used to identify distinct subgroups within a population based on their longitudinal patterns.
  • While widely adopted, the reliability of GBTM findings, particularly in complex or simulated datasets, requires careful examination.

Purpose of the Study:

  • To investigate the susceptibility of Group-based trajectory modelling (GBTM) to generating spurious findings.
  • To evaluate the performance of different model adequacy criteria in assessing classification accuracy within GBTM.

Main Methods:

  • Simulated six plausible scenarios, including two mimicking published analyses, to test GBTM performance.
  • Estimated models with varying numbers of trajectory subgroups (1-10) and selected the best model using the Bayes criterion.
  • Assessed the accuracy of identified trajectory numbers, shapes, and subgroup sizes, comparing average posterior probabilities, relative entropy, and mismatch criteria.

Main Results:

  • GBTM correctly identified the number of trajectories in only two out of six scenarios.
  • Accurate trajectory shapes were identified in four scenarios, while correct mean subgroup sizes were identified in just one scenario.
  • Relative entropy and mismatch criteria demonstrated superior performance over average posterior probability in detecting spurious trajectories.

Conclusions:

  • Group-based trajectory modelling (GBTM) can yield misleading results, particularly when average posterior probability is the sole metric for model evaluation.
  • Researchers are advised to employ a combination of model adequacy criteria to rigorously assess classification accuracy and ensure the validity of GBTM findings.