Matching for factors such as age and sex is a convenient method for minimiz
ing confounding in case-control studies, but it does not allow inferences a
bout the effects of the matching factors unless case ascertainment is virtu
ally complete and the distribution of the matching factors in the source po
pulation is known. When this is so, the effect of a particular factor can b
e estimated by comparing the population distribution of that factor with wh
at is observed in the case series. Such a comparison, however, may itself b
e confounded by other factors that are related to both the matching factors
and the disease under investigation. This article proposes a method for ev
aluating matching factors as risk factors, which uses information on the di
stribution of potential confounders in the reference series and exposure re
lative risk estimates to adjust the person-time proportionality constant in
a Poisson regression model. The method is particularly suited to data sets
in which many of the elementary matching strata contain few or no cases an
d/or controls. It makes use of standard analytic procedures, but requires t
he estimation of an additional variance-covariance component for the estima
ted Poisson regression coefficients. Further factors that may confound the
relationship between exposure and disease are easily accommodated. The meth
od is demonstrated in two examples: a matched case-control study of drugs i
n relation to the rare blood dyscrasia, agranulocytosis,that was conducted
in Europe and Israel, and a case-control study of ovarian cancer in Austral
ia. (C) 2000 Elsevier Science Inc. All rights reserved.