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American Journal of Epidemiology Vol. 125, No. 4: 720-730
Copyright © 1987 by The Johns Hopkins University School of Hygiene and Public Health

research-article

POLYCHOTOMOUS LOGISTIC REGRESSION METHODS FOR MATCHED CASE-CONTROL STUDIES WITH MULTIPLE CASE OR CONTROL GROUPS

KUNG-YEE LIANG1, and WALTER F. STEWART2

1Depertment of Biostatistics, School of Hygiene and Public Health, Johns Hopkins University Baltimore, MD 21205
2Department of Epidemiology, School of Hygiene and Public Health, Johns Hopkins University Baltimore, MD

Send reprint requests to Dr. Liang at this address

Two statistical methods, a polychotomous and pairwise approach, are presented to derive estimates of the relative odds in a matched case-control design when multiple case or control groups are used. Test statistics are derived to determine if the relative odds between groups are different. The polychotomous method is limited to case-control sets, i.e., where data are available on all members of a matched set. in contrast, the pairwise method makes use of data from both complete and incomplete sets. Nonetheless, efficiency calculations show that the polychotomous logistic regression model is more efficient even when 40 per cent of the case-control sets are incomplete. An example using a single dichotomous variable is provided.

epidemiologic methods; retrospective studies; regression analysis; statistics


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