Yelland Lisa N, Sullivan Thomas R, Price David J, Lee Katherine J
School of Public Health, The University of Adelaide, Adelaide, SA, Australia.
South Australian Health and Medical Research Institute, Adelaide, SA, Australia.
Stat Med. 2017 Apr 15;36(8):1227-1239. doi: 10.1002/sim.7201. Epub 2017 Jan 10.
Randomised trials including a mixture of independent and paired data arise in many areas of health research, yet methods for determining the sample size for such trials are lacking. We derive design effects algebraically assuming clustering because of paired data will be taken into account in the analysis using generalised estimating equations with either an independence or exchangeable working correlation structure. Continuous and binary outcomes are considered, along with three different methods of randomisation: cluster randomisation, individual randomisation and randomisation to opposite treatment groups. The design effect is shown to depend on the intracluster correlation coefficient, proportion of observations belonging to a pair, working correlation structure, type of outcome and method of randomisation. The derived design effects are validated through simulation and example calculations are presented to illustrate their use in sample size planning. These design effects will enable appropriate sample size calculations to be performed for future randomised trials including both independent and paired data. Copyright © 2017 John Wiley & Sons, Ltd.
包括独立数据和配对数据的随机试验出现在健康研究的许多领域,但目前缺乏确定此类试验样本量的方法。我们通过代数推导设计效应,假设由于配对数据会在使用具有独立或可交换工作相关结构的广义估计方程进行分析时被考虑在内,因此存在聚类。我们考虑了连续和二元结局,以及三种不同的随机化方法:整群随机化、个体随机化和随机分配到相反治疗组。结果表明,设计效应取决于组内相关系数、属于配对的观察值比例、工作相关结构、结局类型和随机化方法。通过模拟验证了推导的设计效应,并给出了示例计算以说明它们在样本量规划中的应用。这些设计效应将能够为未来包括独立数据和配对数据的随机试验进行适当的样本量计算。版权所有© 2017约翰威立父子有限公司。