Models for Three-Dimensional Contingency Tables with Completely and Partially Cross-Classified Data

We develop models for three-dimensional contingency tables containing both completely and partially cross-classified data for which one of the variables is regarded as dependent and the other two variables are regarded as independent variables. Parameters of interest include the cell probabilities a...

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Bibliographic Details
Published in:Biometrics 1994-03, Vol.50 (1), p.194-203
Main Authors: Williamson, G. David, Haber, Michael
Format: Article
Language:English
Subjects:
Adult
Aged
Biometrics
Biometry - methods
Case-Control Studies
Cervical cancer
Classified information
Covariance matrices
Data Interpretation, Statistical
Disease models
Female
Georgia - epidemiology
Humans
Linear Models
Maximum likelihood estimation
Middle Aged
Missing data
Models, Statistical
Probabilities
Statistics
Three dimensional modeling
Uterine Cervical Neoplasms - epidemiology
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Summary:We develop models for three-dimensional contingency tables containing both completely and partially cross-classified data for which one of the variables is regarded as dependent and the other two variables are regarded as independent variables. Parameters of interest include the cell probabilities and the probabilities that the observations on one or both independent variables are missing. The models allow inferences on these two sets of probabilities to be made independently. Maximum likelihood methods for estimating and testing hypotheses regarding these parameters are described, along with conditional goodness-of-fit test statistics, which display a convenient additivity property. The methodology is applied to cervical cancer data from a case-control study performed in Atlanta, Georgia, 1985-1988.
ISSN:0006-341X
1541-0420
DOI:10.2307/2533209