Statistical methods for relating several exposure factors to several diseases in case-heterogeneity studies

Statistical methods for characterizing risks are now well-developed, although little attention has focussed on the problem of risk identification, as in the surveillance of adverse drug reactions or occupational cancers. In this paper we consider the analysis of studies in which one ascertains and compares cases of several disease groups in terms of exposure histories. We address the problem of adjusting each risk for the confounding effects of all the other risks in the data. Data analysis consists of multidimensional contingency tables or polychotomous logistic regression. The latter approach focuses attention on the exposure-disease relations of primary interest rather than on those among the exposure factors and higher-order interactions, and applies easily to many exposure variables and to continuous exposure variables. We describe a stepwise approach to selecting effects for inclusion in the model. Application to preliminary data from a study aimed at identification of hitherto unsuspected occupational carcinogens illustrates the general approach.