Department of Statistical Science, Southern Methodist University, Dallas, TX, USA.
Division of Biostatistics, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Contemp Clin Trials. 2021 May;104:106336. doi: 10.1016/j.cct.2021.106336. Epub 2021 Mar 6.
Matched-pair cluster randomization design is becoming increasingly used in clinical and health behavioral studies. Investigators often encounter incomplete observations in the data collected. Statistical inference for matched-pair cluster randomization design with incomplete observations has been extensively studied in literature. However, sample size method for such study design is sparsely available. We propose a closed-form sample size formula for matched-pair cluster randomization design with continuous outcomes, based on the generalized estimating equation approach by treating incomplete observations as missing data in a marginal linear model. The sample size formula is flexible to accommodate different correlation structures, missing patterns, and magnitude of missingness. In the presence of missing data, the proposed method would lead to a more accurate sample size estimation than the crude adjustment method. Simulation studies are conducted to evaluate the finite-sample performance of the proposed sample size method under various design configurations. We use bias-corrected variance estimators to address the issue of inflated type I error when the number of clusters per group is small. A real application example of physical fitness study in Ecuadorian adolescents is presented for illustration.
配对聚类随机设计在临床和健康行为研究中越来越多地被使用。研究人员经常在收集的数据中遇到不完整的观察结果。对于具有不完整观察结果的配对聚类随机设计的统计推断在文献中已经得到了广泛的研究。然而，这种研究设计的样本量方法很少。我们提出了一种基于广义估计方程方法的连续结果配对聚类随机设计的闭式样本量公式，通过将不完整的观察结果视为边缘线性模型中的缺失数据来处理。该样本量公式灵活适用于不同的相关结构、缺失模式和缺失程度。在存在缺失数据的情况下，与粗调整方法相比，所提出的方法将导致更准确的样本量估计。模拟研究评估了在各种设计配置下，所提出的样本量方法在有限样本下的性能。当每组的聚类数较小时，我们使用偏校正方差估计来解决Ⅰ型错误膨胀的问题。还提出了厄瓜多尔青少年体质研究的实际应用实例来说明。
