Putting theory into practice: a cluster randomized trial with a small number of clusters.

Many of the difficulties encountered in the design, organization and analysis of cluster randomized trials arise from the dual nature of such trials; that is, they focus on both the cluster and the individual. A trial now in progress to compare three methods of promoting secondary prevention of coronary heart disease in primary care includes only 21 general practices, but 2142 patients, and thus contains the problems of both small and large samples. With only seven practices in each arm, the trial demanded carefully restricted randomization, may be difficult to analyse, and risks loss of power if one practice should drop out. At the same time, the large number of patients makes for an expensive and administratively complex study. The simultaneous demands of clarity and thoroughness point to an analysis at both cluster and individual level. With two different approaches, however, there may be difficulties of presentation, even if the results agree, and additional problems of interpretation if they do not. Finally, practical considerations may conflict with theoretical demands. Since the trial contained a service element, all patients with heart disease had to be included, even though it would have been more efficient to take only a sample of patients from some practices.

[1]  A. Donner,et al.  Randomization by cluster. Sample size requirements and analysis. , 1981, American journal of epidemiology.

[2]  A. Donner,et al.  Analysis of data arising from a stratified design with the cluster as unit of randomization. , 1987, Statistics in medicine.

[3]  A Donner,et al.  The merits of matching in community intervention trials: a cautionary tale. , 1997, Statistics in medicine.

[4]  T D Koepsell,et al.  The effect of matching on the power of randomized community intervention studies. , 1993, Statistics in medicine.

[5]  I. Olkin,et al.  Improving the quality of reporting of randomized controlled trials. The CONSORT statement. , 1996, JAMA.

[6]  Nick Steen,et al.  Sample Size Calculations for Cluster Randomised Trials , 2000, Journal of health services research & policy.

[7]  Stephen Senn,et al.  Statistical Issues in Drug Development , 1997 .

[8]  J. Simpson,et al.  Accounting for cluster randomization: a review of primary prevention trials, 1990 through 1993. , 1995, American journal of public health.

[9]  T. Peters,et al.  What constitutes controlled hypertension? Patient based comparison of hypertension guidelines , 1996, BMJ.

[10]  A Donner,et al.  A methodological review of non-therapeutic intervention trials employing cluster randomization, 1979-1989. , 1990, International journal of epidemiology.

[11]  S M Kerry,et al.  Unequal cluster sizes for trials in English and Welsh general practice: implications for sample size calculations. , 2001, Statistics in medicine.

[12]  H. Davies,et al.  When can odds ratios mislead? , 1998, BMJ.

[13]  A Donner,et al.  Methods for comparing event rates in intervention studies when the unit of allocation is a cluster. , 1994, American journal of epidemiology.

[14]  Paul Aveyard,et al.  Cluster randomised controlled trial of expert system based on the transtheoretical (“stages of change”) model for smoking prevention and cessation in schools , 1999, BMJ.

[15]  B. Charlton,et al.  Health promotion priorities for general practice: constructing and using “indicative prevalences” , 1994, BMJ.

[16]  A. Steptoe,et al.  Behavioural counselling in general practice for the promotion of healthy behaviour among adults at increased risk of coronary heart disease: randomised trial. , 1999, BMJ.

[17]  N. Campbell,et al.  Secondary prevention clinics for coronary heart disease: randomised trial of effect on health , 1998, BMJ.

[18]  J M Bland,et al.  Analysis of a trial randomised in clusters , 1998, BMJ.

[19]  F. Hsieh,et al.  Sample size formulae for intervention studies with the cluster as unit of randomization. , 1988, Statistics in medicine.

[20]  T Heeren,et al.  Robustness of the two independent samples t-test when applied to ordinal scaled data. , 1987, Statistics in medicine.

[21]  P Marsh,et al.  Preventing injuries in children: cluster randomised controlled trial in primary care , 1999, BMJ.