Marginal analysis of a population-based genetic association study of quantitative traits with incomplete longitudinal data

Baojiang Chen, Zhijian Chen, Longyang Wu, Lihua Wang, Grace Yi Yi

Research output: Contribution to journalArticle


A common study to investigate gene-environment interaction is designed to be longitudinal and population-based. Data arising from longitudinal association studies often contain missing responses. Naive analysis without taking missingness into account may produce invalid inference, especially when the missing data mechanism depends on the response process. To address this issue in the analysis concerning gene-environment interaction effects, in this paper, we adopt an inverse probability weighted generalized estimating equations (IPWGEE) approach to conduct statistical inference. This approach is attractive because it does not require full model specification yet it can provide consistent estimates under the missing at random (MAR) mechanism. We utilize this method to analyze data arising from a cardiovascular disease study.

Original languageEnglish (US)
Pages (from-to)109-123
Number of pages15
JournalJournal of the Iranian Statistical Society
Issue number2
Publication statusPublished - Dec 1 2011



  • Generalized estimating equations
  • Genetic association
  • Longitudinal data
  • Missing at random

ASJC Scopus subject areas

  • Statistics and Probability

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