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American Journal of Epidemiology Vol. 119, No. 5: 830-836
Copyright © 1984 by The Johns Hopkins University School of Hygiene and Public Health

other

INTERPRETING MULTIPLE LOGISTIC REGRESSION COEFFICIENTS IN PROSPECTIVE OBSERVATIONAL STUDIES

ROBERT D. ABBOTT1, and RAYMOND J. CARROLL2

1Biometrics Research Branch, National Heart, Lung, and Blood Institute Bethesda, MD 20205.
2Department of Statistics, University of North Carolina Chapel Hill, NC

(Reprint requests to Dr. Robert D. Abbott.)

Abbott, R. D. (National Heart, Lung, and Blood Institute, Bethesda, MD 20205) and R. J. Carroll. Interpreting multiple logistic regression coefficients in prospective observational studies. Am J Epidemiol 1984; 119: 830–6.

Multiple logistic models are frequently used in observational studies to assess the contribution of a risk factor to disease while controlling for one or more covariates. Often, the covariates are correlated with the risk factor, resulting in multiple logistic coefficients that are difficult to interpret. This paper highlights the problem of assessing the magnitude of a multiple logistic coefficient and proposes a supplemental procedure to the usual logistic analysis for describing the relationship between a risk factor and disease. An example is given, along with results that are not apparent when the multiple logistic coefficent is considered alone. Conclusions that are presented are important in biologic studies if describing the effect of a risk factor is influenced by correlation with a covariate.

prospective studies; regression analysis; statistics


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