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Logistic random effects regression models: a comparison of statistical packages for binary and ordinal outcomes.
Baoyue Li1, Hester F Lingsma, Ewout W Steyerberg
1Department of Biostatistics, Erasmus MC, Dr, Molewaterplein 50, Rotterdam, the Netherlands.
BMC Medical Research Methodology
|May 25, 2011
Summary
Logistic random effects models yield similar results across software for large datasets. For smaller datasets, frequentist and Bayesian methods diverge, with implementation choice depending on flexibility and usability needs.
Area of Science:
- Biostatistics
- Statistical Software Comparison
- Multilevel Modeling
Background:
- Logistic random effects models are widely used for analyzing hierarchical data with binary or ordinal outcomes.
- Comparing different statistical software implementations is crucial for accurate and efficient analysis.
Purpose of the Study:
- To compare the performance of various statistical software packages for fitting logistic random effects models.
- To evaluate both frequentist and Bayesian approaches across different software implementations.
Main Methods:
- Utilized individual patient data from 8509 patients across 231 centers from Traumatic Brain Injury (TBI) studies.
- Fitted logistic random effects regression models (binary and ordinal outcomes) using multiple software: R, Stata, SAS, MLwiN, MIXOR, WinBUGS.
- Compared frequentist and Bayesian methods, analyzing full and sub-datasets with varying numbers of level-1 and level-2 units.
Main Results:
- Similar parameter estimates for fixed and random effects were observed for large datasets across software.
- Divergent results between frequentist and Bayesian approaches were noted for sparse datasets (similar numbers of level-1 and level-2 units).
- Software implementations varied significantly in flexibility, computation time, usability, and model evaluation tools.
Conclusions:
- For large datasets, frequentist and Bayesian approaches yield comparable results in logistic random effects models.
- Software selection for large datasets should prioritize flexibility and usability.
- Estimating random effects variances is challenging for small datasets, with frequentist methods often yielding zero estimates and Bayesian methods showing prior dependency.
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