Research Methods & Reporting

The double jeopardy of clustered measurement and cluster randomisation

BMJ 2009; 339 doi: 10.1136/bmj.b2900 (Published 21 August 2009)
Cite this as: BMJ 2009;339:b2900

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  1. Michael S Kramer, professor12,
  2. Richard M Martin, professor34,
  3. Jonathan A C Sterne, professor3,
  4. Stanley Shapiro, professor2,
  5. Mourad Dahhou, statistician1,
  6. Robert W Platt, associate professor12
  1. 1Department of Pediatrics, McGill University Faculty of Medicine, Montreal, Canada
  2. 2Department of Epidemiology and Biostatistics, McGill University Faculty of Medicine
  3. 3Department of Social Medicine, University of Bristol, Bristol
  4. 4MRC Centre for Causal Analysis, University of Bristol
  1. Correspondence to: M S Kramer, Montreal Children’s Hospital, 2300 Tupper Street (Les Tourelles), Montreal, Quebec H3H 1P3 michael.kramer{at}mcgill.ca
  • Accepted 9 March 2009

Michael S Kramer and colleagues suggest that double clustering might explain the negative results of some cluster randomised trials and describe some strategies for avoiding the problem

Cluster randomised trials have become popular for evaluating health service and public health interventions. The clusters are groups of individuals, such as families, schools, clinics, hospitals, or entire communities. Cluster randomised trials provide the rigours of randomisation, while reducing treatment “contamination”; contact between subjects randomised to two (or more) interventions may expose them to both interventions and thus reduce differences in outcome between the groups.1 2 In addition, cluster randomisation is often more feasible than individual randomisation because group dynamics can make it easier to change practices or behaviours within an overall group than to change practices or behaviours among individuals within the same group.

Summary points

  • Clustered measurement occurring in cluster randomised trials will reduce the precision of the results

  • Random allocation of observers or a single observer will avoid clustered measurement but may be impossible for large, geographically dispersed clusters

  • All studies should use standardised measurement techniques and ensure adequate training of observers

  • Pilot studies and monitoring of initial data can identify difficulties in outcome measurement

  • Despite these steps some systematic measurement differences may remain

But cluster randomisation also has some disadvantages. Primary among these is reduced statistical power due to within cluster correlation of outcomes. In other words, individuals within the same cluster are more likely to experience the same study outcome than those in other clusters, irrespective of treatment allocation. This within cluster correlation is usually assessed with the intraclass correlation coefficient (ICC). This coefficient is a measure of how much more similar the values of an outcome are within the same cluster than among different clusters randomised to the same treatment. It is formally defined as the ratio of …

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