title: Estimation methods for marginal and association parameters for longitudinal binary data with nonignorable missing observations.
abstract: In longitudinal studies, missing observations occur commonly. It has been well known that biased results could be produced if missingness is not properly handled in the analysis. Authors have developed many methods with the focus on either incomplete response or missing covariate observations, but rarely on both. The complexity of modeling and computational difficulty would be the major challenges in handling missingness in both response and covariate variables. In this paper, we develop methods using the pairwise likelihood formulation to handle longitudinal binary data with missing observations present in both response and covariate variables. We propose a unified framework to accommodate various types of missing data patterns. We evaluate the performance of the methods empirically under a variety of circumstances. In particular, we investigate issues on efficiency and robustness. We analyze longitudinal data from the National Population Health Study with the use of our methods.
authors: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
authors: Haocheng
authors: H
authors: Li
authors: Grace Y
authors: GY
authors: Yi
mesh_terms: D000368:Aged; D056808:Biostatistics / Q000379:methods*; D003627:Data Interpretation, Statistical; D006306:Health Surveys / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012044:Regression Analysis