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Optimal Designs in Open-Cohort Longitudinal Cluster Randomized Trials With a Continuous Outcome
Jingxia Liu1,2, Fan Li3,4, Xuping Luo2
1Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, Missouri, USA.
Biometrical Journal. Biometrische Zeitschrift
|May 9, 2026
Summary
This study introduces new algorithms for optimizing sample size in open-cohort longitudinal cluster randomized trials (LCRTs). The methods maximize design efficiency by considering cost and correlation parameters for improved trial planning.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Epidemiology
Background:
- Existing methods for longitudinal cluster randomized trials (LCRTs) do not fully optimize open-cohort designs for efficiency.
- Sample size calculations for open-cohort LCRTs with constant churn rates have been previously addressed, but not optimal designs maximizing efficiency.
Purpose of the Study:
- To develop algorithms for optimal sample size determination in open-cohort LCRTs, maximizing design efficiency under a cost-efficiency framework.
- To propose Local Optimal Design (LOD) and MaxiMin optimal design strategies for varying correlation parameter knowledge.
Main Methods:
- Development of algorithms for optimal sample size calculation in open-cohort LCRTs with a fixed number of periods and constant replacement individuals.
- Application of cost-efficiency frameworks to derive optimal cluster-period size, number of clusters, and power.
- Comparison of optimal designs under known and unknown correlation parameters for different LCRT variants (PA-LCRTs, CRXO, SW-CRTs).
Main Results:
- For known correlation parameters, optimal cluster-period size in PA-LCRTs generally decreases then increases with more replacements, while cluster number and power decrease then increase.
- In contrast, for CRXO and SW-CRTs, optimal cluster-period size and churn rate increase, while cluster number and power decrease with more replacements.
- When correlation parameters are unknown, PA-LCRTs and CRXO trials show similar optimal designs with few replacements, with replacement numbers having less impact on cluster-period size than cluster number.
Conclusions:
- The proposed algorithms provide a framework for optimizing sample size and design efficiency in open-cohort LCRTs.
- The findings highlight how the number of replaced individuals impacts optimal design choices differently across various LCRT structures.
- These methods offer practical tools for real-world LCRT planning, demonstrated through two case studies.
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