Abstract
Publisher: School of Statistics, Renmin University of China, Journal: Journal of Data Science, Title: An Analysis of Quasi-complete Binary Data with Logistic Models - Applications to Alcohol Abuse Data, Authors: Mandy C. Webb
Highlights
Logistic regression models are commonly used for modeling binary data in clinical, public health, environmental health and epidemiologic studies
Complete separation can occur with any type of data; quasi-complete separation rarely is present with truly continuous explanatory variables
Using SAS procedures when fitting logistic regression models would have resulted in a warning: “There is a complete separation of data points and that the maximum likelihood estimate does not exist”
Summary
Logistic regression models are commonly used for modeling binary data in clinical, public health, environmental health and epidemiologic studies. A logistic regression model can be fitted using SAS and SPSS, among other softwares Both use iterative procedures involving approximations to obtain parameter estimates for testing hypotheses and predictions. Exact methods are available to remedy these problems using other software such as StatXact or LogXact 4.1, Oster 2002 As these packages are not as readily familiar to researchers when analyzing quasi-complete data with logistic regression models, the guidelines for using two of the more common statistical software packages (SAS and SPSS) should be clearly stated. Silvapulle (1986) obtained a necessary and sufficient condition for the existence of the maximum likelihood estimator for a class of linear regression models for grouped and ungrouped data. This condition has an intuitively simple interpretation.
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