Sun Wenguang, Shults Justine, Leonard Mary
Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, 19034, USA.
Biom J. 2009 Feb;51(1):5-18. doi: 10.1002/bimj.200710493.
Longitudinal trials can yield outcomes that are continuous, binary (yes/no), or are realizations of counts. In this setting we compare three approaches that have been proposed for estimation of the correlation in the framework of generalized estimating equations (GEE): quasi-least squares (QLS), pseudo-likelihood (PL), and an approach we refer to as Wang-Carey (WC). We prove that WC and QLS are identical for the first-order autoregressive AR(1) correlation structure. Using simulations, we then develop guidelines for selection of an appropriate method for analysis of data from a longitudinal trial. In particular, we argue that no method is uniformly superior for analysis of unbalanced and unequally spaced data with a Markov correlation structure. Choice of the best approach will depend on the degree of imbalance and variability in the temporal spacing of measurements, value of the correlation, and type of outcome, e.g. binary or continuous. Finally, we contrast the methods in analysis of a longitudinal study of obesity following renal transplantation in children.
纵向试验可以产生连续、二元（是/否）或计数形式的结果。在这种情况下，我们比较了三种在广义估计方程（GEE）框架下被提出用于估计相关性的方法：拟最小二乘法（QLS）、伪似然法（PL）以及我们称为Wang-Carey（WC）的方法。我们证明了对于一阶自回归AR(1)相关结构，WC和QLS是相同的。然后，我们通过模拟为纵向试验数据分析选择合适方法制定了指南。特别是，我们认为对于具有马尔可夫相关结构的不平衡和不等距数据的分析，没有一种方法是普遍优越的。最佳方法的选择将取决于测量时间间隔的不平衡程度和变异性、相关性的值以及结果的类型，例如二元或连续型。最后，我们在儿童肾移植后肥胖的纵向研究分析中对比了这些方法。
