Semiparametric efficient estimation for incomplete longitudinal Binary data, with application: To smoking trends

Jamie Perin, John S. Preisser, Paul J. Rathouz

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1373-1384
Number of pages12
JournalJournal of the American Statistical Association
Volume104
Issue number488
DOIs
StatePublished - Dec 2009
Externally publishedYes

Keywords

  • Cohort
  • Correlated binary data
  • Dropout
  • Generalized estimating equation
  • Missing data

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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