Tu X M, Kowalski J, Zhang J, Lynch K G, Crits-Christoph P
Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, NY 14642, USA.
Stat Med. 2004 Sep 30;23(18):2799-815. doi: 10.1002/sim.1869.
Existing methods for power and sample size estimation for longitudinal and other clustered study designs have limited applications. In this paper, we review and extend existing approaches to improve these limitations. In particular, we focus on power analysis for the two most popular approaches for clustered data analysis, the generalized estimating equations and the linear mixed-effects models. By basing the derivation of the power function on the asymptotic distribution of the model estimates, the proposed approach provides estimates of power that are consistent with the methods of inference for data analysis. The proposed methodology is illustrated with numerous examples that are motivated by real study designs.
现有的用于纵向研究和其他聚类研究设计的功效及样本量估计方法应用有限。在本文中，我们回顾并扩展现有方法以改善这些局限性。特别地，我们聚焦于聚类数据分析的两种最常用方法——广义估计方程和线性混合效应模型的功效分析。通过基于模型估计的渐近分布推导功效函数，所提出的方法提供的功效估计与数据分析的推断方法一致。所提出的方法通过众多受实际研究设计启发的例子进行了说明。
