Fan Li, PhD Candidate in Biostatistics
Department of Biostatistics and Bioinformatics
Linking Design to Analysis of Cluster Randomized Trials: Covariate Balancing Strategies
Pragmatic clinical trial; Cluster randomized trial; CRT; Covariates; Constrained randomization; Study design
- In cluster-randomized trials, intervention occurs at the cluster level (such as clinics or hospitals) and outcomes are measured at the individual level.
- The goal in a cluster-randomized trial is to leverage design-based control of baseline covariates through stratification, pair matching, and constrained randomization.
- Constrained randomization allows a researcher to assess balance for different allocation schemes and to randomize only within a constrained space with “balanced” schemes.
- Two lessons learned in statistical analysis are model-based inference and permutation inference; in both, analysis of trial results should account for design.
The “Reminder/Recall Immunization Study” example demonstrates 16 randomized counties (clusters), balanced to ensure that urban and rural counties were equally represented in control and treatment groups.
Constrained randomization is often a preferable technique to balance multiple baseline covariates in small cluster-randomized trials because it avoids categorization of continuous covariates (versus stratification).
Software to perform constrained randomization is available in “Stata” and “R” by the Duke biostatistics group.
For information on cluster randomized trials, visit The Living Textbook http://bit.ly/2skjlTW
@PCTGrandRounds, @FrankFanLi; @DukeMedSchool, #randomizedtrials, #pragmatictrials, #pctGR