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Comparison of subject-specific and population averaged models for count data from cluster-unit intervention trials
Mary L Young1, John S Preisser, Bahjat F Qaqish
1Department of Biostatistics, University of North Carolina School of Public Health, Chapel Hill, NC, USA.
Statistical Methods in Medical Research
|May 9, 2007
Summary
This study derives exact relationships between subject-specific and population-averaged models for correlated Poisson count data. Understanding these links is crucial for analyzing cluster-unit intervention trials, especially in public health research.
Area of Science:
- Biostatistics
- Epidemiology
- Public Health Research
Background:
- Cluster-unit intervention trials frequently employ generalized linear mixed models (subject-specific, SS) and generalized estimating equations (population-averaged, PA) to analyze correlated data.
- While both methods account for within-cluster correlations, their fixed effect interpretations, including intervention effects, differ significantly.
- A lack of closed-form mathematical expressions hinders direct comparison and understanding of the relationship between SS and PA parameters.
Purpose of the Study:
- To derive exact mathematical relationships between subject-specific and population-averaged model parameters for correlated Poisson responses.
- To investigate equivalent population-averaged model representations for commonly used subject-specific models in nested, cross-sectional cluster trials with count data.
- To facilitate a clearer understanding of empirical comparisons between subject-specific and population-averaged approaches in cluster trial analysis.
Main Methods:
- Investigated correlated Poisson responses within a log-linear model framework assuming normal random effects.
- Derived exact mathematical relationships between subject-specific and population-averaged parameters.
- Developed equivalent population-averaged model representations for two common subject-specific models used in cluster trial analysis.
Main Results:
- Established exact, closed-form mathematical relationships between subject-specific and population-averaged parameters for correlated Poisson count data.
- Provided equivalent population-averaged model formulations for commonly used subject-specific models in nested cross-sectional cluster trials.
- Demonstrated the application of these mathematical findings using count data from a large non-randomized cluster trial focused on reducing underage drinking.
Conclusions:
- The derived relationships provide essential insights into the connections between subject-specific and population-averaged parameters in correlated count data analysis.
- Understanding these parameter relationships is critical for accurate interpretation and empirical comparison of subject-specific and population-averaged models in cluster-unit intervention trials.
- This work offers a valuable tool for researchers analyzing count data from complex trial designs, enhancing the validity of intervention effect estimation.
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