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StatsToDo : Cluster Randomisation Data Analysis Explained

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Related link :
Cluster Randomisation Data Analysis Program Page
Sample Size for Cluster Randomisation Data Analysis Program Page

Introduction Sample Size Analysis Two Means Two Proportions Technical Considerations References
Cluster randomisation experiments are used in situations where individual research subjects cannot easily be randomly allocated to receive different treatments. In this situation, research subjects are firstly grouped into clusters, and experimental treatments are randomly allocated to clusters so that all members of a cluster receive the same experimental treatment.

Some of the reasons for using the cluster randomisation experiments are

  • When programs of treatment must be applied to a group or a community.
    • In agriculture, when treatments, such as adding fertilisers or pest control, must be applied to a block such as a paddock, a field, or a farm, and cannot be applied to individual plants
    • In education, when interventions are usually applied to a class or a school, and not to individual students
    • In hospitals, where improvements of care are usually applied to a ward or a hospital, and not to individual patients
    • In public health initiatives when an intervention is applied to a community or a region, and not to individual health care consumers
  • When the effect of intervention may contaminate across treatment groups
    • The introduction of new knowledge or techniques to one group which are likely to be copied and used by those in the other group
    • Difficult administrative situations where which subjects belonging to which groups may be confused, such as patients in a hospital ward or students in a class.

The main difference between individual randomisation and cluster randomisation is that members of a cluster may be more similar to each other than to those from different clusters, so the effects of experimental treatment and cluster membership are confounded. There is therefore a need to introduce a correction for this possible confounding, the parameter Intraclass Correlation Coefficient ρ.

ρ, conceptually, is the average of correlations between all possible pairs within a cluster. If subjects are randomly allocated to different clusters and the environments of all clusters are identical, then there should be no correlation between cases in any clusters, and ρ=0. If all members of a cluster produce the same results, then ρ=1.

In both estimating sample size requirement and in the analysis of the data, therefore, the Intraclass Correlation Coefficient ρ is estimated. This is used to adjust the results of standard statistical procedures that are based on individual randomisation, so that the final results are appropriate for cluster randomisation.

Cluster randomisation is a large subject, and StatsToDo provides but the two most basic and commonly used models, that of two group comparison for normally distributed measurements and binomially distributed proportions, as carried out by the algorithms in the Cluster Randomisation Data Analysis Program Page .