Liu G, Liang K Y
Merck Research Laboratories, Blue Bell, Pennsylvania 19422, USA.
Biometrics. 1997 Sep;53(3):937-47.
Correlated data occur frequently in biomedical research. Examples include longitudinal studies, family studies, and ophthalmologic studies. In this paper, we present a method to compute sample sizes and statistical powers for studies involving correlated observations. This is a multivariate extension of the work by Self and Mauritsen (1988, Biometrics 44, 79-86), who derived a sample size and power formula for generalized linear models based on the score statistic. For correlated data, we appeal to a statistic based on the generalized estimating equation method (Liang and Zeger, 1986, Biometrika 73, 13-22). We highlight the additional assumptions needed to deal with correlated data. Some special cases that are commonly seen in practice are discussed, followed by simulation studies.
相关数据在生物医学研究中经常出现。例如包括纵向研究、家族研究和眼科研究。在本文中，我们提出了一种方法，用于计算涉及相关观测值的研究的样本量和统计功效。这是Self和Mauritsen（1988年，《生物统计学》44卷，79 - 86页）工作的多变量扩展，他们基于得分统计量推导出了广义线性模型的样本量和功效公式。对于相关数据，我们采用基于广义估计方程法（Liang和Zeger，1986年，《生物计量学》73卷，13 - 22页）的统计量。我们强调了处理相关数据所需的额外假设。讨论了一些在实际中常见的特殊情况，随后进行了模拟研究。
