Risk assessment for case-control subgroups by polychotomous logistic regression

Abstract

Case-control studies involving more than two disease and referent categories may be analyzed by means of polychotomous logistic regression, an extension of the usual dichotomous logistic regression model. Although the standard method still may be used to compare the several disease subgroups in pairs, the polychotomous approach is advantageous in that it allows simultaneous estimation of the disease-specific parameters and direct hypothesis testing involving multiple disease categories. This is especially useful for assessing whether different disease types have different risk factors. The method is applied to a large case-control study of breast cancer involving three disease categories for which both categoric and continuous risk factors are considered. Substantive epidemiologic interpretation of polychotomous regression outputs is emphasized, as well as providing illustration of the practical aspects of the statistical method.