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Population-averaged and cluster-specific models for clustered ordinal response data

T R Ten Have¹, J R Landis, J Hartzel

¹Center for Biostatistics and Epidemiology, Hershey Medical Center, Pennsylvania State University, Hershey 17033, USA.

Statistics in Medicine

|December 15, 1996

Summary

This summary is machine-generated.

We compared two statistical models for clustered ordinal data. A relationship between population-averaged and cluster-specific parameters found for binary data also applies to cumulative logit models, aiding analysis of clinical trial data.

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Area of Science:

Statistics
Biostatistics
Clinical Trials

Background:

Clustered ordinal data present unique statistical challenges.
Existing models for binary data may not directly translate to ordinal outcomes.
Accurate modeling is crucial for interpreting results from complex clinical trial designs.

Purpose of the Study:

To compare population-averaged and cluster-specific statistical models for analyzing clustered ordinal data.
To investigate the applicability of known parameter relationships from binary logistic models to cumulative logit models.
To apply these comparative models to data from cross-over clinical trials.

Main Methods:

Generalized estimating equations (GEE) for population-averaged cumulative logit models.
Constrained equations maximum likelihood estimation for population-averaged models.
Mixed effects modeling for cluster-specific cumulative logit models.

Main Results:

The relationship between population-averaged and cluster-specific parameters observed in binary logistic regression was found to hold for cumulative logit models.
Both modeling approaches yielded comparable insights when applied to the clinical trial data.
The study validates the extension of parameter relationship principles across different data types.

Conclusions:

Population-averaged and cluster-specific models provide consistent results for clustered ordinal data under cumulative logit assumptions.
The established parameter relationship offers a valuable theoretical link between different modeling frameworks.
These findings enhance the statistical toolkit for analyzing complex longitudinal and clustered data in clinical research.