Sample size calculations for 3-level cluster randomized trials
Sample size calculations for 3-level cluster randomized trials
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DOI：
10.1177/1740774508096476
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发表时间：
2008-01-01
期刊：
影响因子：
2.7
通讯作者：
Borm, George F.
中科院分区：
文献类型：
--
作者：
Teerenstra, Steven;Moerbeek, Mirjam;Borm, George F.
背景：采用三个而不是两个水平的聚类随机试验的首次应用开始出现在卫生研究中，例如，在比较实施最佳实践指南的不同策略的试验中。在这些试验中，该战略在卫生保健单位（“分组”）实施，旨在改变在该单位工作的卫生保健专业人员（“受试者”）的行为，而在患者层面衡量效果（“评估”）。目的指导分组数量、每组受试者数量和每组受试者评估次数的选择。方法推导出样本容量公式，研究样本分配对所需聚类的功率或数量的影响。结果所需样本量是不存在相关性的样本量与两个方差膨胀因子（VIFs）的乘积，这两个方差膨胀因子分别描述了受试者内评价和受试者内评价的聚类。由于每个VIF都以可解释的Pearson相关性表示，因此可以合并主题知识。此外，这些皮尔逊相关性与可比较的2水平集群随机试验的簇内相关性（ICCs）相关。获得公式来指导样本分配（集群数量、受试者数量和评估），以最小化总样本大小、最小化集群数量或在给定预算约束的情况下最大化权力。从三水平聚类试验中对方差成分或ICCs的经验估计很少，这限制了可靠的供电。当以Pearson相关性参数化时，两个方差膨胀因子可以定量地了解聚类数量、受试者数量和评价对权力的影响。此外，当缺乏来自试点或可比的3水平研究的方差成分或icc的经验估计时，可以将主题知识以及来自2水平聚类随机试验的icc纳入样本量计算。临床试验2008；5: 486 - 495。http://ctj.sagepub.com
Background The first applications of cluster randomized trials with three instead of two levels are beginning to appear in health research, for instance, in trials where different strategies to implement best-practice guidelines are compared. In such trials, the strategy is implemented in health care units ('clusters') and aims at changing the behavior of health care professionals working in this unit ('subjects'), while the effects are measured at patient level ('evaluations').Purpose To guide the choice of number of clusters, number of subjects per cluster, and number of evaluations per subject.Methods We derive a sample size formula and investigate the influence of sample allocation on power or number of clusters required.Results The required sample size is the product of the sample size in absence of correlation and two variance inflation factors (VIFs) that describe the clustering of evaluations within subjects and of subjects within cluster, respectively. Because each VIF is expressed in terms of an interpretable Pearson correlation, subject matter knowledge can be incorporated. Moreover, these Pearson's correlations are related to intracluster correlations (ICCs) from comparable, but 2-level cluster randomized trials. Formulas are obtained to guide the sample allocation (number of clusters, subjects, and evaluations) for minimizing total sample size, minimizing the number of clusters, or maximizing power given a budget constraint.Limitations Empirical estimates of variance components or ICCs from 3-level cluster trials are scarce which limits reliably powering.Conclusions When parameterized in terms of Pearson correlations, the two variance inflation factors give quantitative insight into the impact of the number of clusters, subjects and evaluations on power. Moreover, subject matter knowledge as well as ICCs from 2-level cluster randomized trials can be incorporated in the sample size calculation, when empirical estimates of variance components or ICCs from a pilot or comparable 3-level study are lacking. Clinical Trials 2008; 5: 486-495. http://ctj.sagepub.com