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.
The work done by Georgia Southern faculty 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×K2×2×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.
“Estimates for cell counts and common odds ratio in three-way contingency tables by homogeneous log-linear models with missing data,” was published in Advances in Statistical Analysis in July.
Dr. Haresch Rochani, assistant professor of biostatistics, Dr. Robert Vogel, dual department chair for biostatistics and epidemiology, Dr. Hani Samawi, professor of biostatistics, at the Jiann-Ping Hsu College of Public Health Georgia Southern University were the authors for this study.