Chen Yong, Ning Jing, Cai Chunyan
Division of Biostatistics, The University of Texas School of Public Health, 1200 Pressler Street, Houston, TX 77030, USA
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Biostatistics. 2015 Oct;16(4):727-39. doi: 10.1093/biostatistics/kxv008. Epub 2015 Mar 25.
In longitudinal data analyses, the observation times are often assumed to be independent of the outcomes. In applications in which this assumption is violated, the standard inferential approach of using the generalized estimating equations may lead to biased inference. Current methods require the correct specification of either the observation time process or the repeated measure process with a correct covariance structure. In this article, we construct a novel pairwise likelihood method for longitudinal data that allows for dependence between observation times and outcomes. This method investigates the marginal covariate effects on the repeated measure process, while leaving the probability structure of the observation time process unspecified. The novelty of this method is that it yields consistent estimator of the marginal covariate effects without specification of the observation time process or the covariance structure of the repeated measures process. Large sample properties of the regression coefficient estimates and a pairwise likelihood ratio test procedure are established. Simulation studies demonstrate that the proposed method performs well in finite samples. An analysis of weight loss data from a web-based program is presented to illustrate the proposed method.
在纵向数据分析中，通常假定观察时间与结果相互独立。在违背这一假设的应用中，使用广义估计方程的标准推断方法可能会导致有偏推断。当前的方法要求正确指定观察时间过程或具有正确协方差结构的重复测量过程。在本文中，我们为纵向数据构建了一种新颖的成对似然方法，该方法允许观察时间与结果之间存在相关性。此方法研究边际协变量对重复测量过程的影响，同时不对观察时间过程的概率结构进行指定。该方法的新颖之处在于，无需指定观察时间过程或重复测量过程的协方差结构，就能得出边际协变量效应的一致估计量。建立了回归系数估计的大样本性质以及成对似然比检验程序。模拟研究表明，所提出的方法在有限样本中表现良好。本文给出了对一个基于网络项目的体重减轻数据的分析，以说明所提出的方法。
