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Studies with group treatments required special power calculations, allocation methods, and statistical analyses
Miriam C Faes1, Miriam F Reelick, Marieke Perry
1Department of Geriatric Medicine, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands. m.faes@ger.umcn.nl
Journal of Clinical Epidemiology
|August 23, 2011
Summary
Group-based trials need specialized methods for treatment allocation and analysis. Minimization strategies offer superior balance and predictability, crucial for accurate study power calculations and reliable results.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Health Services Research
Background:
- Interventions delivered to groups (e.g., group exercise) require specific analytical considerations.
- The group structure impacts trial power and necessitates tailored design and analysis methods.
Purpose of the Study:
- To provide optimal methods for designing and analyzing group-based trials.
- To evaluate various treatment allocation methods for group-based interventions.
Main Methods:
- Described and simulated treatment allocation methods: unrestricted randomization, stratification, permuted block randomization, deterministic minimization, and optimal batchwise minimization (OBM).
- Derived a formula for sample size calculation.
- Described appropriate multilevel analysis methods accounting for group structure.
Main Results:
- Stratification, deterministic minimization, and OBM demonstrated significantly lower risk of imbalance compared to unrestricted randomization and permuted block randomization.
- Optimal batchwise minimization (OBM) provided unpredictable treatment allocation.
- Multilevel models are essential for sample size calculation and analysis in group-based trials.
Conclusions:
- Group-based trials necessitate adapted treatment allocation, power calculation, and analysis methods.
- Minimization is recommended for overall balance; stratification is a good alternative for few prognostic factors.
- OBM is complex but beneficial for trials with many prognostic factors, offering superior predictability.
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