Estimates for Cell Counts and Common Odds Ratio in Three-Way Contingency Tables by Homogeneous Log-Linear Models with Missing Data
Document Type
Article
Publication Date
1-2017
Publication Title
AStA Advances in Statistical Analysis
DOI
10.1007/s10182-016-0275-y
ISSN
1863-818X
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 2×2×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.
Recommended Citation
Rochani, Haresh, Robert L. Vogel, Hani M. Samawi, Daniel Linder.
2017.
"Estimates for Cell Counts and Common Odds Ratio in Three-Way Contingency Tables by Homogeneous Log-Linear Models with Missing Data."
AStA Advances in Statistical Analysis, 101 (1): 51-65.
doi: 10.1007/s10182-016-0275-y
https://digitalcommons.georgiasouthern.edu/biostat-facpubs/146
