Cluster randomized trials (CRTs) differ from individually randomized trials in that the unit of randomization is something other than the individual participant or patient. CRTs are in common use in areas such as education and public health research; they are particularly well suited to testing differences in a method or approach to patient care (as opposed to evaluating the physiological effects of an intervention).

Watch the video module: Understanding Clustering in Cluster Randomized Trials

Why Choose Cluster Randomization?

There are several reasons why a CRT design might be preferred to or more suitable than an individually randomized trial. First, a CRT might be preferred when the target of the intervention is a collective or system rather than a particular person, such as a patient. For example, while an individually randomized trial may be better suited to determining whether a novel therapy works in patients with a given disease or condition, a CRT is better able to evaluate whether a new standard of care, guideline recommendation, or other practice-wide, hospital-wide, or system-wide change affects patient outcomes. Second, a CRT might be preferred when there is a significant potential for contamination in the study. Contamination occurs when aspects of an intervention are adopted by members of the group that was randomized to not receive that intervention. (See also "What Is Contamination, and Why Does it Matter?" immediately below).

There are also compelling practical reasons for randomizing clusters rather than individuals (Cook et al 2016). For example, in a trial comparing 12-hour nursing shifts to 8-hour shifts, implementing these protocols at the patient level would be nearly impossible. In this case, randomizing wards or floors would be more practical and would accommodate the need to avoid contamination.

What Is Contamination, and Why Does it Matter?

The most compelling reason to randomize at the cluster level rather than at the individual level is the potential for contamination, whereby participants within a cluster are likely to be treated similarly and hence exhibit similar outcomes.

When contamination occurs during a clinical trial, it dilutes the observed differences between comparators and can affect the reliability and validity of the study.

Case Examples of Contamination 

• Example 1:Participants who share the same provider in a trial comparing weight-loss strategies may meet each other in the waiting room and communicate about their respective strategies, or the provider might not be able to adapt to coaching differently depending on the randomization. Some participants in each group might even adopt elements of both strategies, and neither group would demonstrate the impact of its intended strategy. Randomization at the provider level, with each provider coaching only a single strategies, would reduce the risk of contamination.

• Example 2: A trial evaluating a campaign designed to reduce nosocomial infections by encouraging better staff handwashing practices might include posters in each of the rooms. Staff generally cover several rooms on a floor and would be exposed to the posters, which would likely change their behavior if the posters were effective. Not only would it be infeasible to randomize at the provider or patient level, doing so would minimize the difference between groups due to the contamination. The campaign might then be declared unsuccessful despite actually having had a positive effect. The solution would be to randomize different areas of the hospital (taking care to consider potential confounding as described in the coming sections), with only half of the areas receiving the posters.

Although avoiding contamination is one of the most important reasons for using CRT designs, pragmatic concerns can dominate the need for cluster randomization when it is practically impossible to randomize at an individual level.