Consistency Across Sites: Design and Analysis of Multicentre Studies
Plan standardisation, randomisation, data quality, clustering and centre effects in multicentre clinical research.
Why use multiple centres?
Multicentre studies can recruit faster, include more diverse participants and improve generalisability. They also introduce variation in measurement, protocol implementation, clustering and operations that must be addressed prospectively.
Standardise critical processes
Use a shared operations manual for eligibility, time zero, visit windows, laboratories, outcome definitions and data entry. Centralise protocol questions so that every site receives the same interpretation.
Randomisation and centre effects
Centre may be a stratification factor, but creating a separate stratum for many tiny sites can be unstable. Central randomisation and variable block sizes can protect allocation. Analysis may use fixed centre effects, random effects or cluster-robust standard errors depending on the estimand and site structure.
Data quality
Maintain a common data dictionary.
Monitor missingness, queries and deviations by site.
Consider central laboratories or blinded endpoint adjudication.
Focus risk-based monitoring on critical data and processes.
Preserve audit trails and role-based access.
Current GCP principles
ICH E6(R3) emphasises quality by design, fitness for purpose and proportionate risk management. Resources should focus on factors critical to participant safety and reliable conclusions.
References
Accessed 20 June 2026.