Impact of non-uniform correlation structure on sample size and power in multiple-period cluster randomised trials
Impact of non-uniform correlation structure on sample size and power in multiple-period cluster randomised trials
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DOI：
10.1177/0962280217734981
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发表时间：
2019-03-01
影响因子：
2.3
通讯作者：
Forbes, A. B.
中科院分区：
文献类型：
--
作者：
Kasza, J.;Hemming, K.;Forbes, A. B.
逐步楔形试验和整群随机交叉试验是在多个时间段进行的整群随机设计的例子，在健康研究中使用频率越来越高。最近对这两种设计的系统评价表明，在使用随机截距混合模型进行数据分析时，通常会考虑群内相关性，这意味着同一群中的任何两个个体之间的相关性是恒定的，无论它们在时间上相隔多远：期间内和期间之间的群内相关性被假设为相同。最近提出的扩展允许期间内和期间之间的聚类内相关性不同，虽然这些方法要求所有期间之间的聚类内相关性是相同的，这可能不是在所有情况下都合适。出于拟议的重症监护群集随机试验，我们提出了一种替代的相关性结构，重复的横截面多阶段群集随机试验中，期间内群集内的相关性被允许衰减取决于测量之间的距离。我们目前的治疗效果估计量的方差变化的衰减，调查的后果的变化衰减的样本量规划为阶梯楔形，集群交叉和多阶段平行臂集群随机试验。我们还研究了假设不变的期间内集群内的相关性，而不是衰减的期间内集群内的相关性的影响。我们的研究结果表明，在某些设计配置，包括一个对应于拟议的试验，相关性衰减可以有一个重要的影响方差的治疗效果估计，因此对样本量和功率。R Shiny应用程序允许读者交互式地探索相关性衰减的影响。
Stepped wedge and cluster randomised crossover trials are examples of cluster randomised designs conducted over multiple time periods that are being used with increasing frequency in health research. Recent systematic reviews of both of these designs indicate that the within-cluster correlation is typically taken account of in the analysis of data using a random intercept mixed model, implying a constant correlation between any two individuals in the same cluster no matter how far apart in time they are measured: within-period and between-period intra-cluster correlations are assumed to be identical. Recently proposed extensions allow the within- and between-period intra-cluster correlations to differ, although these methods require that all between-period intra-cluster correlations are identical, which may not be appropriate in all situations. Motivated by a proposed intensive care cluster randomised trial, we propose an alternative correlation structure for repeated cross-sectional multiple-period cluster randomised trials in which the between-period intra-cluster correlation is allowed to decay depending on the distance between measurements. We present results for the variance of treatment effect estimators for varying amounts of decay, investigating the consequences of the variation in decay on sample size planning for stepped wedge, cluster crossover and multiple-period parallel-arm cluster randomised trials. We also investigate the impact of assuming constant between-period intra-cluster correlations instead of decaying between-period intra-cluster correlations. Our results indicate that in certain design configurations, including the one corresponding to the proposed trial, a correlation decay can have an important impact on variances of treatment effect estimators, and hence on sample size and power. An R Shiny app allows readers to interactively explore the impact of correlation decay.