Managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: A survey to establish current practice

Elizabeth J Conroy, Jane M Blazeby, Girvan Burnside, Jonathan A Cook, Carrol Gamble

Research output: Contribution to journalArticle (Academic Journal)peer-review

12 Downloads (Pure)

Abstract

Background
Patient outcomes can depend on the treating centre, or health professional, delivering the intervention. Health professional’s skill in delivery improves with experience, meaning that outcomes may be associated with learning. Considering differences in intervention delivery at trial design will ensure that any appropriate adjustments can be made during analysis. This work aimed to establish practice for the allowance of clustering and learning effects in the design and analysis of randomised multicentre trials.

Methods
A survey that drew upon quotes from existing guidelines, references to relevant publications, and example trial scenarios was delivered. Registered UK Clinical Research Collaborative registered Clinical Trials Units were invited to participate.

Results
Forty-four Units participated (N=50). Clustering was managed through design by stratification, more commonly by centre than treatment provider. Managing learning by design through defining a minimum expertise level for treatment provider was common (89%). One-third reported experience of expertise-based designs. The majority of Units had adjusted for clustering during analysis, although approaches varied. Analysis of learning was rarely performed for the main analysis (n=1), although was explored by other means. Insight behind the approaches used within and reasons for, or against, alternative approaches were provided.

Conclusions
Widespread awareness of challenges in designing and analysing multicentre trials is identified. Approaches used, and opinions on these, vary both across Units and within, indicating that approaches are dependent on the type of trial. Agreeing principles to guide trial design and analysis across a range of realistic clinical scenarios should be considered.
Original languageEnglish
Article number433
Number of pages13
JournalTrials
DOIs
Publication statusPublished - 27 May 2020

Keywords

  • trials
  • clinical trials unit
  • clinical trial
  • randomised controlled trial
  • complex intervention
  • surgical intervention
  • trial design
  • trial analysis
  • survey
  • clustering
  • learning

Fingerprint

Dive into the research topics of 'Managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: A survey to establish current practice'. Together they form a unique fingerprint.

Cite this