This article discusses the growing interest in the use of biomarker data in environmental health research and considers some of the challenging statistical issues that arise. We specify a modeling framework that links environmental exposure, biomarkers and outcome, and discuss in conceptual terms how such a formulation could be used to inform dose response modeling for the purpose of quantitative risk assessment. We then analyse some biomarker data from a case-control study designed to elucidate the mechanisms of smoking induced lung cancer. Because of sample size limitations, we use a likelihood-based analysis which subsumes both cohort and case-control designs as special cases. Our analysis allows us to 1) investigate the extent to which the markers explain the pathway from exposure to outcome; 2) quantify the degree to which biomarker data can improve on predicting outcome over and above exposure; and 3) estimate the association among multiple markers.
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