A Review of Some Statistical Methods for Covariance Analysis of Categorical Data

Three general methods for covariance analysis of categorical data are reviewed and applied to an example from a clinical trial in rheumatoid arthritis. The three methods considered are randomization-model nonparametric procedures, maximum likelihood logistic regression, and weighted least squares an...

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Bibliographic Details
Published in:Biometrics 1982-09, Vol.38 (3), p.563-595
Main Authors: Koch, Gary G., Amara, Ingrid A., Davis, Gordon W., Gillings, Dennis B.
Format: Article
Language:English
Subjects:
Adult
Age Factors
Aged
Analysis of Variance
Arthritis, Rheumatoid - drug therapy
Biometrics
Clinical Trials as Topic
Covariance
Covariance matrices
Humans
Integers
Least squares
Middle Aged
Modeling
Models, Biological
Papers on the Analysis of Covariance
Parametric models
Placebos
Random allocation
Standard error
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Summary:Three general methods for covariance analysis of categorical data are reviewed and applied to an example from a clinical trial in rheumatoid arthritis. The three methods considered are randomization-model nonparametric procedures, maximum likelihood logistic regression, and weighted least squares analysis of correlated marginal functions. A fourth heuristic approach, the unweighted linear model analysis, is an approximate procedure but it is easy to implement. The assumptions and statistical issues for each method are discussed so as to emphasize philosophical differences between their rationales. Attention is given to computational differences, but it is shown that the methods lead to similar results for analogous problems. It is argued that the essential differences between the methods lie in their underlying assumptions and the generality of the conclusions which may be drawn.
ISSN:0006-341X
1541-0420
DOI:10.2307/2530041