Explicit estimates for cell counts and modeling the missing data indicators in
three-way contingency table by log-linear models
 
   Lead Investigator:    Haresh Rochani
   Institution      :    Georgia Southern University
   E-Mail           :    hrochani@georgiasouthern.edu
   Proposal ID      :    490


   Publications:


1  Rochani HD, Samawi HM, Linder DF
   Maximum likelihood estimates by Homogenous log-linear models for three-way contingency tables with Missing data with application to neuropathology data
   2015   Abstract  Paper presented at the APHA 2016, Chicago , 

   ACCEPTED





   Proposal Description:
   Missing observations in cross-classified data are an extremely common problem in the process of research in public health, clinical sciences and social sciences. Ignorance of missing values in the analysis can produce biased results and low statistical power. The focus of this research is to expand Baker, Rosenberger and Dersimonian (BRD) model approach to compute the explicit maximum likelihood estimates for cell counts for three-way cross-classified data. In case of missing observations, derivation of explicit cell counts for three-way table with supplementary margins can be obtained by controlling the missingness in third variable and by modeling the missing-data indicators using homogeneous log-linear models. The model based approach for contingency tables has the advantage of providing the information of missing data mechanisms. Previous methods for contingency tables with supplementary margins required an iterative algorithm, however, expected cell counts for complete cells as well as missing cells can be obtained by simple algebraic formula.