UTILIZING CONCENTRATION-RESPONSE DATA FROM INDIVIDUAL COMPONENTS TO DETECT STATISTICALLY SIGNIFICANT DEPARTURES FROM ADDITIVITY IN CHEMICAL-MIXTURES

The classical approach for detecting interactions in a combination of drugs or chemicals is that of the isobologram, quantified and generali zed by Berenbaum (1981, Advances in Cancer Research 35, 269-335). In t his formulation it is assumed that contours of constant response of th e dose-response surface are planar if the compounds do nor interact. B uilding upon this approach, this paper develops methodology for detect ing and characterizing departures from additivity. Reflecting the loca l rather than global nature of departure from additivity, this methodo logy only requires dose-response data for the individual components an d the specific combinations(s) of interest. This is in contrast to the larger experiments required to estimate the multidimensional dose-res ponse surface for the combination. Procedures for incorporating data f rom multiple control groups are developed for a fixed-effects model, a random-effects model, and through use of a generalized estimating equ ations approach. An example is given that illustrates the application of these techniques to the analysis of a mixture of polycyclic aromati c hydrocarbons found in kerosene soot.