COMPARATIVE EXPOSURE RATIOS - A NONPARAMETRIC, MULTIFACTOR TECHNIQUE FOR CASE-CONTROL STUDIES

The odds ratio in a two-by-two table is widely used in case-control st udies to measure association between disease and a binary risk factor. In this article we propose a more general measure of association, the comparative exposure ratio (CER), which is the ratio of the number of case-control pairs where the case has greater exposure divided by the number where the control has greater exposure. In simple cases, the C ER is an odds ratio or a weighted combination of odds ratios. In more general cases a CER continues to measure association even when an odds ratio computation is not feasible. Moreover, CERs improve on odds rat ios in several ways: they do not require binary risk factors, or a cho ice of the scale of measurement of continuous risk factors; they make it possible to investigate multiple risk factors simultaneously, witho ut multivariate parametric assumptions; they also can be used to detec t patterns that might indicate possible causal pathways. We illustrate how various choices of the definition of 'greater exposure' make the CER a powerful and flexible tool. We give expressions for confidence i ntervals for CERs, and verify in a pilot simulation that they are vali d. Finally, we illustrate with a case-control study of cervical dyspla sia how exploratory inference using CERs can be carried out. (This res earch was partially supported by grants from the National Cancer Insti tute, CA 41108 and CA 25702.)