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A mixed-effects regression model for three-level ordinal response data.

Rema Raman1, Donald Hedeker

  • 1Division of Biostatistics, University of California at San Diego, La Jolla, CA 92093-0949, USA. rema@ucsd.edu

Statistics in Medicine
|October 6, 2005
PubMed
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This study introduces a three-level mixed-effects regression model for complex nested data common in medical and behavioral sciences. The model incorporates random effects and flexible odds specifications, enhancing analysis of multi-level studies.

Area of Science:

  • Statistics
  • Biostatistics
  • Behavioral Sciences

Background:

  • Three-level nested data structures are prevalent in medical and behavioral research, such as multi-center trials and twin studies.
  • Accurate statistical modeling is crucial for analyzing complex hierarchical data, accounting for dependencies at multiple levels.

Purpose of the Study:

  • To describe a novel three-level mixed-effects regression model for analyzing hierarchical data.
  • To incorporate random effects at multiple levels and develop both proportional and non-proportional odds models.

Main Methods:

  • Development of a three-level mixed-effects regression model with random effects at levels 2 and 3.
  • Implementation of proportional and non-proportional odds models, allowing for varying covariate effects across cumulative logits.

Related Experiment Videos

  • Utilizing maximum marginal likelihood (MML) estimation with Gauss-Hermite quadrature for integrating random effects.

    • Main Results:

    • The proposed model effectively handles complex three-level nested data structures.
    • The inclusion of random effects and flexible odds specifications improves the analysis of hierarchical data.
    • Demonstrated utility through applications in a school-based smoking prevention trial and an Alzheimer's disease clinical trial.

    Conclusions:

    • The described three-level mixed-effects model provides a robust framework for analyzing nested data in behavioral and medical sciences.
    • The model's flexibility in handling proportional and non-proportional odds enhances its applicability to diverse research scenarios.
    • This approach offers improved analytical capabilities for complex hierarchical study designs.