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Estimation methods for marginal and association parameters for longitudinal binary data with nonignorable missing observations
Author(s) -
Li Haocheng,
Yi Grace Y.
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5536
Subject(s) - missing data , covariate , pairwise comparison , computer science , statistics , robustness (evolution) , econometrics , binary data , data mining , binary number , mathematics , machine learning , biochemistry , chemistry , arithmetic , gene
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. Copyright © 2012 John Wiley & Sons, Ltd.