Term of Award
Spring 2014
Degree Name
Doctor of Public Health in Biostatistics (Dr.P.H.)
Document Type and Release Option
Dissertation (restricted to Georgia Southern)
Copyright Statement / License for Reuse
Digital Commons@Georgia Southern License
Department
Department of Biostatistics (COPH)
Committee Chair
Robert L. Vogel
Committee Member 1
Hani Samawi
Committee Member 2
Daniel Linder
Abstract
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 study 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. 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. 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. Simulation study with Source of knowledge of cancer data illustrate that how well the explicit maximum likelihood estimates can produce consistent results in idyllic circumstances. Application of the BRD model approach to Slovenian public opinion survey data reveals the effect of smaller sample size to the validity of the method for three-way table.
Recommended Citation
Haresh Rochani, Robert L. Vogel, Hani M. Samawi and Daniel F. Linder. "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 Vol. 101 Iss. 1 (2016) p. 51 - 65. DOI: https://doi.org/10.1007/s10182-016-0275-y
Research Data and Supplementary Material
No