Power calculations for generalized linear models in observational longitudinal studies: a simulation approach in SAS.
Power calculations for generalized linear models in observational longitudinal studies: a simulation approach in SAS.
复制标题
观察纵向研究中广义线性模型的功效计算：SAS 中的模拟方法。
DOI：
10.1016/j.cmpb.2006.07.011
复制
发表时间：
2006
影响因子：
6.1
通讯作者：
Delfino,RalphJ
中科院分区：
文献类型：
--
作者：
Gastanaga,VictorM;McLaren,ChristineE;Delfino,RalphJ
纵向研究引起的重复测量在应用研究中经常出现。在重复测量的背景下，计算功率的方法可用于实验设置，其中感兴趣的协变量是一个离散处理指标。然而，对于在流行病学和观察性研究中常见的具有非零簇内相关性的广义线性模型，没有封闭形式的表达式来计算功率，在这些研究中，感兴趣的协变量随时间而变化，通常是在连续尺度上测量的，并且研究人员控制了几个潜在的混杂因素。我们描述了一种用于计算功率的蒙特卡罗模拟方法，并说明了它在实践中经常遇到的两种模型中的应用，即正态线性混合模型和逻辑回归模型，这两种模型都具有重复测量和非零簇内相关。这种方法可以用来计算改变研究者控制的各种模拟条件对功率的影响，如样本量、聚类内相关结构、最小有意义差异检测和分布假设。
Repeated measurements arising from longitudinal studies occur frequently in applied research. Methods to calculate power in the context of repeated measures are available for experimental settings where the covariate of interest is a discrete treatment indicator. However, no closed form expression exists to calculate power for generalized linear models with non-zero within-cluster correlation that are common in epidemiological and observational studies in which the covariate of interest varies over time and is often measured on a continuous scale, and where the researchers control for several potential confounders. We describe a Monte Carlo simulation approach conducted to calculate power, and illustrate its application in two models frequently encountered in practice, the normal linear mixed model, and the logistic regression model, both with repeated measurements and non-zero within-cluster correlation. This approach can be used to calculate the effect on power of changing various simulation conditions controlled by the researcher, such as sample size, within-cluster correlation structure, smallest meaningful difference to detect, and distributional assumptions.