Experimental Designs and Randomization Schemes
Section 8
Pair Matching and Stratification With Cluster Designs
As with individually randomized trials, a number of considerations need to be addressed up front for CRTs to avoid downstream problems. In particular, potential confounding is always an issue. For example, if older patients are more likely than younger patients to acquire nosocomial infections, it would be important to ensure that one of the arms of the trial is not more likely to consist of older patients. In this example, if the clusters are hospital wards, there should be some assurance of balance in the average ages of patients in the wards assigned to one arm compared to the other. Sometimes there are several potential confounders.
Two popular mechanisms for achieving balance are pair matching and stratification. With pair matching, clusters are paired in terms of their potential confounders and then within each pair, one cluster is randomized to receive one of the arms and the other cluster receives the opposite arm. For example, considering age and sex as potential confounders, clusters would be matched into pairs such that the average age and the percentage of women in the cluster are approximately equal. Likewise, the sizes of the 2 clusters should be similar. Stratification is a generalization of pair matching, in that strata are formed based on the potential confounders; within each stratum, a randomization scheme that ensures balance is developed. For example, if there are 11 clusters in a stratum, the randomization would assign 5 clusters to one arm and 6 to the other. However, when there are several confounders, it can be difficult to use stratification or pair matching.
Constrained Randomization
Another method that is increasingly being studied and implemented in CRTs is constrained randomization. Exploiting the fact that all of the clusters are identified before randomization, each cluster can be characterized in terms of the levels of several potential confounders. For any possible randomization of this set of clusters, a balance metric is applied to “measure” the amount of imbalance that would exist if that particular randomization were applied. From this “randomization space,” a single randomization scheme is selected. (See the Covariate-Constrained Randomization section in this chapter of the Living Textbook.)
For more information about considerations affecting study design decisions, see also the Designing With Implementation and Dissemination in Mind chapter of the Living Textbook.
SECTIONS
sections
- Introduction
- Statistical Design Considerations
- Cluster Randomized Trials
- Alternative Cluster Randomized Designs
- Stepped-Wedge Designs
- Choosing Between Cluster and Individual Randomization
- Covariate-Constrained Randomization
- Pair Matching and Stratification With Cluster Designs
- Concealment and Masking
- Designing to Avoid Identification Bias
- Additional Resources
Resources
Pragmatic and Group-Randomized Trials in Public Health and Medicine—Part 3. Analysis Approaches
Online course From the NIH Office of Disease Prevention
Pair-Matching vs Stratification in Cluster-Randomized Trials
Guidance document from the Biostatistics and Study Design Core
Advanced Methods for Primary Care Research: The Stepped Wedge Design
Presentation from the Agency for Healthcare Research and Quality providing a technical overview of applications of the stepped-wedge design in clinical research
current section : Pair Matching and Stratification With Cluster Designs
- Introduction
- Statistical Design Considerations
- Cluster Randomized Trials
- Alternative Cluster Randomized Designs
- Stepped-Wedge Designs
- Choosing Between Cluster and Individual Randomization
- Covariate-Constrained Randomization
- Pair Matching and Stratification With Cluster Designs
- Concealment and Masking
- Designing to Avoid Identification Bias
- Additional Resources