Interim data monitoring in cluster randomised trials: Practical issues and a case study

Area of Science:

• Clinical Trials Methodology
• Biostatistics
• Public Health Research

Background:

• Guidance for interim monitoring is abundant for individually randomized trials, but practical implementation for cluster randomized trials is less developed.
• Cluster trials possess distinct features necessitating tailored monitoring strategies compared to individual trials.
• Methodological literature on extending individual trial monitoring to cluster trials exists, yet practical guidance remains scarce.

Purpose of the Study:

• To outline the methodological and practical challenges in the interim monitoring of cluster randomized trials.
• To apply these considerations to a real-world case study, the E-MOTIVE study.
• To facilitate planning and promote discussion for improved interim monitoring in future cluster randomized trials.

Main Methods:

• The E-MOTIVE study, an 80-cluster randomized trial, serves as a case study.
• The data monitoring plan includes monitoring sample size assumptions, selection bias, and primary outcomes.
• Methods include comparing individual characteristics for selection bias and using the Haybittle-Peto approach for interim outcome assessment.

Main Results:

• Interim monitoring of cluster trials requires careful consideration of nuisance parameters and their uncertain estimation.
• Sample size re-estimation utility can be limited by practical or funding constraints in cluster trials.
• Interim monitoring is crucial for identifying selection bias, especially in trials with post-randomization recruitment.

Conclusions:

• Interim analyses in cluster trials differ significantly from those in individually randomized trials.
• The pragmatic nature of cluster trials may reduce the relevance of adherence monitoring and statistical outcome testing.
• Addressing practical and methodological challenges is key to effective interim monitoring of cluster randomized trials.