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Inference for the treatment effect in multiple-period cluster randomised trials when random effect correlation
Jessica Kasza1, Andrew B Forbes1
1Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Statistical Methods in Medical Research
|September 8, 2018
Summary
Incorrectly assuming constant within-cluster correlation in stepped wedge and cluster crossover trials can lead to flawed treatment effect estimates. This study highlights the importance of accurate correlation structure modeling for reliable clinical trial results.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Epidemiology
Background:
- Multiple-period cluster randomized trials (e.g., stepped wedge, cluster crossover) are increasingly common.
- Accurate specification of within-cluster correlation structure is crucial for trial design and analysis.
- Standard statistical software often lacks support for complex correlation decay models.
Purpose of the Study:
- To analytically investigate the impact of misspecifying within-cluster correlation structure on treatment effect estimation.
- To evaluate the consequences of omitting correlation decay in stepped wedge and cluster crossover trials.
Main Methods:
- Analytical examination of the variance of the treatment effect estimator.
- Focus on the implications of assuming identical versus decaying within-cluster correlations.
Main Results:
- Incorrectly omitting a decay in within-cluster correlation can significantly impact the variance of the treatment effect estimator.
- Misspecification of the correlation structure can lead to erroneous conclusions about treatment effects.
Conclusions:
- The choice of within-cluster correlation model is critical in multiple-period cluster randomized trials.
- Failure to account for correlation decay may compromise the validity of findings in stepped wedge and cluster crossover designs.
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