Cluster Randomization Trials
3. Statistical Implications
The goal of this exercise is to recognize the purpose and implications of cluster randomized trials, reinforcing for the user why one might adopt cluster randomization.
Case Study Example:
A trial for the prevention of heart disease in factory workers is being planned. The main purpose of the trial will be to evaluate a strategy in which information concerning coronary risk factors is provided to the workers by a health counselor. The investigators have chosen factories as the unit of randomization.
Questions 1 out of 3: In the case described, determine what the motivating factor(s) for adopting cluster randomization might have been.
Possible Motivating Factor(s)
Risk of contamination
Choosing to randomize clusters (factories) rather than individuals will minimize the likelihood of subjects in different intervention groups sharing information.
Secure Informed Consent
Cluster randomization will minimize problems related to securing informed consent.
Factory authorities may find it ethically compromising to offer preventive advice to some workers in their factory but not others.
Providing advice to all eligible factory workers in naturally available groups would be more administratively convenient and less costly than providing such advice on a one-to-one basis.
Reduce Loss to follow Up
Cluster randomization is likely to reduce the anticipated loss to follow-up rate.
User can select ‘How did I do’ button. If the user does not answer the question correctly, the user has a choice to select ‘Restart Question 1’ or ‘Show Correct Answers’ buttons. Once the user has answered all the questions correctly or selected the ‘Show Correct Answers’ button they can select the ‘Continue’ button.
Questions 2 out of 3: Identify what potential problems may arise from cluster randomization.
Individual Level Risk Factors
Randomization at the cluster level can provide baseline comparability with respect to cluster level risk factors but not with respect to individual level risk factors.
Since the investigators will be obliged to inform half the participating factories that they will not receive the intervention, some factories may decline to accept the assignment of their workers to the control group. This could create problems of selection bias.
If workers who already have heart disease are not removed from the trial at baseline, the effect of the intervention will be diluted.
Factories willing to participate in the trial may be those who are most likely to comply with the intervention. Moreover the “healthy worker effect” must be recognized, where occupational cohorts differ from the general population in their health status. These factors could affect the generalizabilty of the trial results.
Statistical analyses at the cluster (factory) level are fully efficient only if the intracluster correlation coefficient is 1.0.
User can select ‘How did I do’ button. If the user does not answer the question correctly, the user has a choice to select ‘Restart Question 2’ or ‘Show Correct Answers’ buttons. Once the user has answered all the questions correctly or selected the ‘Show Correct Answers’ button they can select the ‘Continue’ button.
Question 3 of 3: Identify what statistical implications might arise from methodological challenges.
In order to properly estimate the required sample size for this trial, it will be necessary to obtain an a priori estimate of the intracluster correlation coefficient.
Magnitude of Effect
With the intervention provided at the cluster level to relatively healthy individuals, its intensity may be less than if applied at the level of the individual worker. This must be taken into account in assessing the magnitude of the likely intervention effect.
It is necessary to address concerns regarding the ecological fallacy in interpreting the trial results.
Depending on the degree of between-factory heterogeneity and the number of factories to be enrolled in the trial, a decision will need to be made concerning the desirability of at least some stratification by baseline risk factors.
User can select ‘How did I do’ button. If the user does not answer the question correctly, the user has a choice to select ‘Restart Question 3’ or ‘Show Correct Answers’ buttons. Once the user has answered all the questions correctly or selected the ‘Show Correct Answers’ button they can select the ‘Finish’ button. The user can select ‘Restart Exercise.’