American Journal of Epidemiology Vol. 129, No. 1: 191-204
Copyright © 1989 by The Johns Hopkins University School of Hygiene and Public Health
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ORDINAL REGRESSION MODELS FOR EPIDEMIOLOGIC DATA
From the School of Occupational Health 1130 Pine Avenue West, Montreal, Quebec, Canada H3A 1A3
Send reprint requests to Dr. Ben G. Armstrong at this address.
Armstrong, B. G., and M. Sloan (School of Occupational Health, McGill U., Montreal, Quebec, Canada H3A 1A3). Ordinal models for epidemiologic data. Am J Epidemiol 1989;129:191204.
Health status Is often measured In epidemiologic studies on an ordinal scale, but data of this type are generally reduced for analysis to a single dichotomy. Several statistical models have been developed to make full use of Information in ordinal response data, but have not been much used in analyzing epIdemIologic studies. The authors discuss two of these statistical modelsthe cumulative odds model and the continuation ratio model. They may be interpreted In terms of odds ratios, can account for confoundIng variables, have clear and testable assumptions, and have parameters that may be estimated and hypotheses that may be tested using available statistical packages. However, calculations of asymptotic relative efficiency and results of simulations showed that simple logistic regression applied to dichotomized responses can In some realistic situations have more than 75% of the efficiency of ordinal regression models, but only If the ordinal scale is collapsed into a dichotomy close to the optimal point. The application of the proposed models to data from a study of chest x rays of workers exposed to mineral fibers confirmed that they are easy to use and interpret, but gave results quite similar to those obtained using simple logistic regression after dichotomizing outcome in the conventional way.
asbestosis; regression analysis; statistics
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