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Repeated measures regression mixture models.

Minjung Kim1, M Lee Van Horn2, Thomas Jaki3

  • 1Department of Educational Studies, Ohio State University, Columbus, OH, USA. Kim.7144@osu.edu.

Behavior Research Methods
|June 2, 2019
PubMed
Summary
This summary is machine-generated.

Repeated measures regression mixture models significantly improve the identification of effect heterogeneity compared to traditional methods. This approach enhances model performance and allows for smaller sample sizes in research.

Keywords:
Heterogeneous effectsRegression mixture modelsRepeated measuresSample size

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Area of Science:

  • Statistics
  • Psychometrics
  • Behavioral Science

Background:

  • Regression mixture models are valuable for understanding effect heterogeneity.
  • Existing methods often require large sample sizes.
  • Repeated measures data offer potential for improved analysis.

Purpose of the Study:

  • To extend regression mixture models to a repeated measures framework.
  • To evaluate the performance of repeated measures regression mixture models against traditional and averaged models.
  • To assess the impact of repeated measures on class enumeration and parameter estimation accuracy.

Main Methods:

  • Comparison of three regression mixture model specifications: single-outcome, repeated measures (3, 5, 7 measures), and averaged single-outcome.
  • Evaluation of model performance based on class enumeration and parameter estimate accuracy.
  • Application of the repeated measures approach to data from the Sleep Research Project.

Main Results:

  • Repeated measures regression mixture models substantially outperformed single-outcome and averaged models in class enumeration.
  • Reduced bias in parameter estimates was observed with repeated measures models.
  • The repeated measures approach enables accurate analysis with sample sizes as low as 200 participants, given sufficient repeated measures (e.g., seven).

Conclusions:

  • Repeated measures regression mixture models offer superior performance for exploring heterogeneity in effects.
  • This method enhances statistical power and reduces sample size requirements.
  • The proposed approach is applicable to various research areas utilizing longitudinal or repeated measures data.