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Analysis of cluster randomized trials in primary care: a practical approach.

Author(s): Ian Steen, Joy M. Campbell, Diana Eccles, J Grimshaw, J. Mollison

Publication date: 2000-03-31

Keywords: Cluster Analysis, Data Interpretation, Statistical, Family Practice, standards, Health Services Research, methods, Humans, Models, Statistical, Practice Guidelines as Topic, Primary Health Care, Randomized Controlled Trials as Topic, Referral and Consultation, Reproducibility of Results, Research Design, Urology

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Abstract

Cluster randomized trials increasingly are being used in health services research and in primary care, yet the majority of these trials do not account appropriately for the clustering in their analysis. We review the main implications of adopting a cluster randomized design in primary care and highlight the practical application of appropriate analytical techniques. The application of different analytical techniques is demonstrated through the use of empirical data from a primary care-based case study. Inappropriate analysis of cluster trials can lead to the presentation of inaccurate results and hence potentially misleading conclusions. We have demonstrated that adjustment for clustering can be applied to real-life data and we encourage more routine adoption of appropriate analytical techniques.

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Most cited references 7

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Methods for comparing event rates in intervention studies when the unit of allocation is a cluster.

A. Donner, N Klar (1994)

The aim of many research investigations is to compare the proportion of individuals in each of several groups that have a certain characteristic. The unit of allocation for such investigations is often an intact social unit, as in randomizing families, medical practices, schools, or entire communities, to different intervention groups. Standard statistical methods are not appropriate for these designs, since they do not take into account the dependencies among individuals within the same cluster. The authors review the strengths and weaknesses of several approaches for dealing with this problem, using data from a school-based smoking cessation trial. A principal conclusion is that the choice of method should depend on whether or not random allocation is used in the assignment of interventions.