Estimates for cell counts and common odds ratio in three-way contingency tables by homogeneous log-linear models with missing data

Haresh Rochani; Robert L. Vogel; Hani M. Samawi; Daniel Linder

doi:10.1007/s10182-016-0275-y

Estimates for cell counts and common odds ratio in three-way contingency tables by homogeneous log-linear models with missing data

Haresh Rochani, Robert L. Vogel, Hani M. Samawi, Daniel Linder

Georgia Southern University

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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.

Original language	English
Pages (from-to)	51-65
Number of pages	15
Journal	AStA Advances in Statistical Analysis
Volume	101
Issue number	1
DOIs	https://doi.org/10.1007/s10182-016-0275-y
State	Published - Jan 1 2017

Scopus Subject Areas

Analysis
Applied Mathematics
Economics and Econometrics
Statistics and Probability
Social Sciences (miscellaneous)
Modeling and Simulation

Keywords

Common odds ratio
Contingency table
Cross-classified data
Log-linear model
Maximum likelihood method
Missing data
Three-way table

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s10182-016-0275-y

Cite this

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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.",

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AU - Linder, Daniel

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AB - 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.

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KW - Contingency table

KW - Cross-classified data

KW - Log-linear model

KW - Maximum likelihood method

KW - Missing data

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JO - AStA Advances in Statistical Analysis

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Estimates for cell counts and common odds ratio in three-way contingency tables by homogeneous log-linear models with missing data

Abstract

Scopus Subject Areas

Keywords

UN SDGs

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