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Accounting for intraclass correlations and controlling for baseline differences in a cluster-randomised
Xian-Jin Xie1, Marita G Titler, William R Clarke
1Department of Clinical Sciences-Division of Biostatistics and Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. xian-jin.xie@utsouthwestern.edu
Worldviews on Evidence-Based Nursing
|June 19, 2008
Summary
Cluster-randomised designs require accounting for intra-class correlations and baseline differences in data analysis. Mixed or marginal models can be used, with the choice depending on whether the focus is on population or individual outcomes.
Area of Science:
- Health Services Research
- Biostatistics
- Clinical Trials
Background:
- Cluster-randomised designs are increasingly used in health interventions for convenience and to minimize contamination.
- These designs present analytical challenges, including accounting for intra-class correlations and baseline imbalances.
- Pain management intervention studies exemplify these challenges.
Purpose of the Study:
- To present two data analysis strategies for cluster-randomised designs that account for baseline differences.
- To illustrate these strategies using a pain management intervention study.
- To guide the choice between mixed and marginal models based on research questions.
Main Methods:
- Utilizing mixed models with SAS PROC MIXED.
- Employing marginal models with Generalized Estimating Equations (GEE) via SAS PROC GENMOD.
- Both methods adjust for intra-class correlation and baseline covariates.
Main Results:
- Parameter estimates and standard errors can be comparable between mixed and marginal models under specific link functions (identity or log).
- Interpretations and suitability differ significantly based on the chosen model.
- Accounting for baseline measures is crucial when pre-existing differences exist between groups.
Conclusions:
- Adjusting for intra-class correlation is essential when evaluating intervention effects in cluster-randomised trials.
- The choice between mixed (random effects) and marginal models depends on whether the primary interest is in population-averaged or individual-level effects.
- Both approaches are valuable for analysing cluster-randomised data, but address distinct inferential goals.
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