Geographic pair-matching in large-scale cluster randomized trials.
Geographic pair-matching in large-scale cluster randomized trials.
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大规模整群随机试验中的地理配对。
DOI：
10.1101/2023.04.30.23289317
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
ColfordJr,Jo
中科院分区：
文献类型：
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作者：
Arnold,BenjaminF;Rerolle,Francois;Tedijanto,Christine;Njenga,SammyM;Rahman,Mahbubur;Ercumen,Ayse;Mertens,Andrew;Pickering,Amy;Lin,Audrie;Arnold,CharlesD;Das,Kishor;Stewart,ChristineP;Null,Clair;Luby,StephenP;ColfordJr,Jo
整群随机试验通常用于研究大规模的公共卫生干预措施。在大型试验中，即使是统计效率的微小改进也会对所需的样本量和成本产生深远影响。位置将许多社会人口和环境特征整合成一个单一的、现成的特征。在这里，我们通过重新分析孟加拉国和肯尼亚的两个大规模营养和环境干预试验，表明按地理位置配对导致14个儿童健康结果的统计效率大幅提高，这些结果涵盖了生长，发育和传染病。配对的相对效率在所有结果中均≥1.1，并且经常超过2.0，这意味着不匹配的试验需要招募至少两倍的聚类，以达到与地理配对设计相同的精度水平。我们还表明，地理配对匹配的设计，使估计细尺度，空间变化的影响异质性在最小的假设。我们的研究结果表明，在大规模的集群随机试验中，地理配对的广泛，实质性的好处。
Cluster randomized trials are often used to study large-scale public health interventions. In large trials, even small improvements in statistical efficiency can have profound impacts on the required sample size and cost. Location integrates many socio-demographic and environmental characteristics into a single, readily available feature. Here we show that pair matching by geographic location leads to substantial gains in statistical efficiency for 14 child health outcomes that span growth, development, and infectious disease through a re-analysis of two large-scale trials of nutritional and environmental interventions in Bangladesh and Kenya. Relative efficiencies from pair matching are ≥1.1 for all outcomes and regularly exceed 2.0, meaning an unmatched trial would need to enroll at least twice as many clusters to achieve the same level of precision as the geographically pair matched design. We also show that geographically pair matched designs enable estimation of fine-scale, spatially varying effect heterogeneity under minimal assumptions. Our results demonstrate broad, substantial benefits of geographic pair matching in large-scale, cluster randomized trials.