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American Journal of Epidemiology Vol. 125, No. 3: 524-535
Copyright © 1987 by The Johns Hopkins University School of Hygiene and Public Health

research-article

ANALYSIS OF PROPORTIONATE MORTALITY DATA USING LOGISTIC REGRESSION MODELS

JAMES M. ROBINS1, and DON BLEVINS1

1Occupational Health Program, Harvard School of Public Health 665 Huntington Ave., Boston, MA 02115

Reprint requests to Dr. James M. Robins

When only proportionate mortality data are available to an investigator studying the effect of an exposure on a particular cause of death, controls must be selected from among persons dying of other causes believed to be uninfluenced by the exposure under study. When qualitative or quantitative estImates of exposure history can be obtained for the deceased individuals, it is shown that one can use logistic regression models for the mortality odds to efficiently estimate the effect of exposure while controlling for relevant confounding factors by incorporating a priori information on baseline mortality rates available from US life tables. The proposed method is used to reanalyze data from a cohort of arsenic-exposed workers in a Montana copper smelter.

epidemiologic methods; models, theoretical; mortality; retrospective studies


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