Semiparametric Efficient Estimation for Incomplete Longitudinal Binary Data, With Application to Smoking Trends

• 成果类型: Article
• 署名作者: Perin, Jamie; Preisser, John S.; Rathouz, Paul J.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of Chicago
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.ap08527
• 发表日期: 2009
• 页码: 1373-1384
• 关键词: weighted estimating equations; generalized linear-models; regression-models; Missing Data; repeated outcomes; drop-outs; follow-up; inference; nonresponse; responses

摘要

Incomplete longitudinal data often are analyzed with estimating equations for inference on a parameter from a marginal mean regression model. Generalized estimating equations, although commonly used for incomplete longitudinal data, are invalid for data that are not missing completely at random. There exists a class of inverse probability weighted estimating equations that are valid under dropouts missing at random, including an easy-to-implement but inefficient member. A relatively computationally complex semiparametric efficient estimator in this class has been applied to continuous data. A specific form of this estimator is developed for binary data and used as a benchmark for assessing the efficiency of the simpler estimator in a simulation study. Both are applied in the estimation of 15-year cigarette smoking trends in the United States from a cohort of 5077 young adults. The results suggest that declines in smoking from previous reports have been exaggerated.