Hayes R J, Bennett S
MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, UK.
Int J Epidemiol. 1999 Apr;28(2):319-26. doi: 10.1093/ije/28.2.319.
Cluster-randomized trials, in which health interventions are allocated randomly to intact clusters or communities rather than to individual subjects, are increasingly being used to evaluate disease control strategies both in industrialized and in developing countries. Sample size computations for such trials need to take into account between-cluster variation, but field epidemiologists find it difficult to obtain simple guidance on such procedures.
In this paper, we provide simple formulae for sample size determination for both unmatched and pair-matched trials. Outcomes considered include rates per person-year, proportions and means. For simplicity, formulae are expressed in terms of the coefficient of variation (SD/mean) of cluster rates, proportions or means. Guidance is also given on the estimation of this value, with or without the use of prior data on between-cluster variation.
The methods are illustrated using two case studies: an unmatched trial of the impact of impregnated bednets on child mortality in Kenya, and a pair-matched trial of improved sexually-transmitted disease (STD) treatment services for HIV prevention in Tanzania.
整群随机试验是将卫生干预措施随机分配给完整的群组或社区,而非个体受试者,目前在工业化国家和发展中国家越来越多地用于评估疾病控制策略。此类试验的样本量计算需要考虑群组间的变异,但现场流行病学家发现很难获得关于此类程序的简单指导。
在本文中,我们提供了用于确定非匹配和配对匹配试验样本量的简单公式。所考虑的结局包括每人年发病率、比例和均值。为简便起见,公式以群组发病率、比例或均值的变异系数(标准差/均值)表示。还给出了在有无群组间变异的先验数据情况下对该值进行估计的指导。
通过两个案例研究来说明这些方法:一个关于在肯尼亚使用浸渍蚊帐对儿童死亡率影响的非匹配试验,以及一个关于在坦桑尼亚为预防艾滋病毒而改善性传播疾病(STD)治疗服务的配对匹配试验。