4.3 Article Proceedings Paper

Differential recruitment in a cluster randomized trial in primary care: the experience of the UK back pain, exercise, active management and manipulation (UK BEAM) feasibility study

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CLINICAL TRIALS
卷 2, 期 2, 页码 119-124

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SAGE PUBLICATIONS LTD
DOI: 10.1191/1740774505cn073oa

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Cluster randomized trials, which randomize groups of patients rather than individuals, are commonly used to evaluate healthcare interventions such as training programmes targeted at health professionals. This article reports the dangers of randomizing entire primary care practices when participants cannot be identified before randomization, as shown by a UK national trial. Method The UK BEAM trial, a national cluster randomized 3 x 2 x 2 factorial trial, was designed to evaluate three treatments for back pain in primary care: active management; randomized by practice; and spinal manipulation and exercise classes, both randomized by individual. Results Two hundred and thirty-one participants were recruited in the feasibility study, 165 (141 % of expected recruitment) from active (management) practices but only 66 (54% of expected recruitment) from traditional (management) practices. The participants in active practices were significantly different from those in traditional practices, notably in suffering from milder back pain. Conclusions The feasibility study highlighted the dangers of randomizing clusters when individuals cannot be identified beforehand. Different numbers and types of participants were recruited in the two types of cluster. This differential recruitment led us to change the main trial design by abandoning practice level randomization. Instead all practices were trained in active management to maximize recruitment. Ideally cluster randomized trials should identify patients beforehand, to minimize the chance of selection bias. If this is not possible, patient recruitment should be independent in both intervention and control clusters. Pilot studies are especially important for cluster randomized trials, to identify unforeseen problems.

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