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 and Daniel F. Linder
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Haresh D. Rochani: Georgia Southern University
Robert L. Vogel: Georgia Southern University
Hani M. Samawi: Georgia Southern University
Daniel F. Linder: Augusta University

AStA Advances in Statistical Analysis, 2017, vol. 101, issue 1, No 3, 65 pages

Abstract: Abstract 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 $$ 2\times 2 \times K $$ 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.

Keywords: Contingency table; Cross-classified data; Log-linear model; Maximum likelihood method; Missing data; Common odds ratio; Three-way table (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10182-016-0275-y

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