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首页> 外文期刊>Controlled clinical trials >Mounting a community-randomized trial: sample size, matching, selection, and randomization issues in PRISM.
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Mounting a community-randomized trial: sample size, matching, selection, and randomization issues in PRISM.

机译：开展社区随机试验：PRISM中的样本量，匹配，选择和随机化问题。

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This paper discusses some of the processes for establishing a large cluster-randomized trial of a community and primary care intervention in 16 local government areas in Victoria, Australia. The development of the trial in terms of design factors such as sample size estimates and the selection and randomization of communities to intervention or comparison is described. The intervention program to be implemented in Program of Resources, Information and Support for Mothers (PRISM) was conceived as a whole community approach to improving support for all mothers in the first 12 months after birth. A cluster-randomized trial was thus the design of choice from the outset. With a limited number of communities available, a matched-pair design with eight pairs was chosen. Sample size estimates, adjusting for the cluster randomization and the pair-matched design, showed that with eight pairs, on average, 800 women from each community would need to respond to provide sufficient power to determine a 3% reduction in the prevalence of maternal depression 6 months after birth-a reduction deemed to be a worthwhile impact of the intervention to be reliably detected at 80% power. The process of selecting suitable communities and matching them into pairs required careful collection of data on numbers of births, size of the local government areas (LGAs), and an assessment of the capacity of communities to implement the intervention. Ways of dealing with boundary issues associated with potential contamination are discussed. Methods for the selection of feasible configurations of sets of pairs and the ultimate allocation to intervention or comparison are provided in detail. Ultimately, all such studies are a balancing act between selecting the minimum number of communities to detect a meaningful outcome effect of an intervention and the maximum size budget and other resources allow.
机译：本文讨论了在澳大利亚维多利亚州的16个地方政府地区建立社区和初级保健干预措施的大型集群随机试验的一些过程。描述了根据设计因素（例如样本量估计）以及社区的选择和随机化以进行干预或比较来进行试验的进展。计划在母亲的资源，信息和支持计划（PRISM）中实施的干预计划是整个社区的方法，旨在提高出生后前12个月对所有母亲的支持。因此，从一开始就选择了一项集群随机试验。在可用社区数量有限的情况下，选择了八对配对的配对设计。样本量估计值（针对集群随机化和配对设计进行了调整）显示，平均每个社区有800对女性需要八对女性做出响应，以提供足够的力量来确定孕产妇抑郁症的患病率降低3％出生后6个月-减少干预被认为是值得的干预措施，只要以80％的功率进行可靠检测即可。选择合适的社区并将其配对的过程需要仔细收集有关出生人数，地方政府区域（LGAs）规模的数据，并评估社区实施干预措施的能力。讨论了处理与潜在污染相关的边界问题的方法。详细提供了选择对对的可行配置以及对干预或比较进行最终分配的方法。最终，所有这些研究都是在选择最少数量的社区以检测干预措施的有意义结果效果与最大预算和其他资源允许量之间取得平衡的行为。

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