Estimates for cell counts and common odds ratio in three-way contingency tables by homogeneous log-linear models with missing data

Haresh D. Rochani, Robert L. Vogel, Hani M. Samawi, Daniel F. Linder

Research output: Contribution to journalArticle

1 Scopus citations


Missing observations often occur in cross-classified data collected during observational, clinical, and public health studies. Inappropriate treatment of missing data can reduce statistical power and give biased results. This work extends the Baker, Rosenberger and Dersimonian modeling approach to compute maximum likelihood estimates for cell counts in three-way tables with missing data, and studies the association between two dichotomous variables while controlling for a third variable in 2 × 2 × K tables. This approach is applied to the Behavioral Risk Factor Surveillance System data. Simulation studies are used to investigate the efficiency of estimation of the common odds ratio.

Original languageEnglish (US)
Pages (from-to)51-65
Number of pages15
JournalAStA Advances in Statistical Analysis
Issue number1
Publication statusPublished - Jan 1 2017



  • Common odds ratio
  • Contingency table
  • Cross-classified data
  • Log-linear model
  • Maximum likelihood method
  • Missing data
  • Three-way table

ASJC Scopus subject areas

  • Analysis
  • Statistics and Probability
  • Modeling and Simulation
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Applied Mathematics

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