  The paper proposes to analyze epidemiological data using regression models
which enable subject-matter (epidemiological) interpretation of such data
whether with uncorrelated or correlated predictors. To this end, response
functions should include not only terms linear in predictors but also higher
order ones (e.g. quadratic and cross terms). For epidemiological interpretation
of a regression model, the suggestion is to construct conditional functions
derived from the general regression function with the values of all predictor
variables held fixed excepting one predictor. Unlike the conventional
techniques based on linear-predictor models in which the coefficient at any
variable is interpreted, our approach proposes to interpret this conditional
function, which is multivariate for any predictor being dependent on the values
of all the other predictors. It is such functions that can describe
relationships between Y and a predictor that have different forms in different
predictor domains. The paper discusses differences in the interpretation of the
proposed conditional functions between cases involving correlated and
uncorrelated predictor variables. The construction and analysis of regression
models for epidemiological and environmental data are illustrated with
examples.
