Modelling complex mixtures in epidemiologic analysis: additive versus relative measures for differential effectiveness.

OBJECTIVES: Mixed exposures are often combined into single exposure measures using weighting factors. This occurs for many complex mixtures in environmental and occupational epidemiology including multiple congeners, air pollutants and unique forms of ionising radiation, among others. METHODS: The weights used for combining exposures are most often determined from experimental animal and cellular research. However, evidence from observational research is necessary to support their use in risk analyses, since results from experimental research do not directly translate to observational epidemiology. RESULTS: Using simulated data, we show that ratio-based relative weights cannot be reliably estimated from observational research. As a solution to this problem, we propose an approach for estimating differences in effectiveness of distinct exposures based on their excess effectiveness compared with a reference exposure. CONCLUSIONS: This alternative is easy to calculate and provides reliable estimates of differences in effectiveness of distinct exposures. This is important to regulatory bodies using relative measures for policy decisions, as well as practicing epidemiologists conducting risk analyses.