title: Regression analysis of longitudinal data with irregular and informative observation times.
abstract: 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.
authors: Division of Biostatistics, The University of Texas School of Public Health, 1200 Pressler Street, Houston, TX 77030, USA chenyong1203@gmail.com.
authors: Yong
authors: Y
authors: Chen
authors: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
authors: Jing
authors: J
authors: Ning
authors: Division of Clinical and Translational Sciences, Department of Internal Medicine, The University of Texas Medical School at Houston, Houston, TX 77030, USA.
authors: Chunyan
authors: C
authors: Cai
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D012044:Regression Analysis; D012107:Research Design; D015431:Weight Loss
keywords: Informative observation time; Irregular observation time; Longitudinal data analysis; Outcome-dependent sampling; Pairwise likelihood