Hybrid sample size calculations for cluster randomised trials using assurance
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View abstract on PubMed
Summary
This summary is machine-generated.Determining sample size for cluster trials is complex. Bayesian assurance offers a more robust method than traditional power calculations by incorporating parameter uncertainty, leading to more reliable trial sample sizes.
Area Of Science
- Biostatistics
- Clinical Trials Methodology
- Health Research Methods
Background
- Sample size determination for cluster randomized trials (CRTs) is challenging due to the need for accurate intra-cluster correlation coefficient (ICC) estimation.
- Traditional power calculations are sensitive to ICC inaccuracies, potentially leading to under- or over-powered trials.
- Imprecise ICC estimates often arise from studies with few clusters, complicating sample size planning.
Purpose Of The Study
- To propose a hybrid Bayesian assurance and frequentist approach for sample size determination in CRTs.
- To incorporate uncertainty in key parameters like ICC, standard deviation, and coefficient of variation of cluster size.
- To demonstrate the approach using a CRT design for post-stroke incontinence.
Main Methods
- Utilized Bayesian assurance as an alternative to traditional power, incorporating prior distributions for key parameters.
- Specified prior distributions for standard deviation, ICC, and coefficient of variation of cluster size.
- Applied the method to a CRT for post-stroke incontinence, comparing results with standard power calculations.
Main Results
- Bayesian assurance allows sample size calculation using elicited prior distributions for ICC, unlike power calculations that use single point estimates.
- The proposed approach avoids sample size misspecification when prior distributions differ significantly, even with similar medians.
- Accounting for uncertainty in all nuisance parameters did not substantially increase the required sample size.
Conclusions
- Bayesian assurance enhances understanding of trial success probability and provides more robust sample sizes against parameter uncertainty.
- This method is particularly beneficial when reliable parameter estimates are difficult to obtain.
- The hybrid approach offers a more reliable alternative for sample size determination in CRTs.
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