title: Statistical models for low dose exposure.
abstract: Extrapolation of health risks from high to low doses has received a considerable amount of attention in carcinogenic risk assessment over decades. Fitting statistical dose-response models to experimental data collected at high doses and use of the fitted model for estimating effects at low doses lead to quite different risk predictions. Dissatisfaction with this procedure was formulated both by toxicologists who saw a deficit of biological knowledge in the models as well as by risk modelers who saw the need of mechanistically-based stochastic modeling. This contribution summarizes the present status of low dose modeling and the determination of the shape of dose-response curves. We will address the controversial issues of the appropriateness of threshold models, the estimation of no observed adverse effect levels (NOAEL), and their relevance for low dose modeling. We will distinguish between quantal dose-response models for tumor incidence and models of the more informative age/time dependent tumor incidence. The multistage model and the two-stage model of clonal expansion are considered as dose-response models accounting for biological mechanisms. Problems of the identifiability of mechanisms are addressed, the relation between administered dose and effective target dose is illustrated by examples, and the recently proposed Benchmark Dose concept for risk assessment is presented with its consequences for mechanistic modeling and statistical estimation.
authors.affiliation: Biostatistics Unit-R0700, German Cancer Research Center, Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany. edler@dkfz-heidelberg.de
authors.forename: L
authors.initials: L
authors.lastname: Edler
authors.forename: A
authors.initials: A
authors.lastname: Kopp-Schneider
mesh_terms: D000367:Age Factors; D000818:Animals; D002273:Carcinogens / Q000633:toxicity*; D004305:Dose-Response Relationship, Drug; D004781:Environmental Exposure; D006801:Humans; D008452:Maximum Allowable Concentration; D015233:Models, Statistical; D009369:Neoplasms / Q000139:chemically induced*; D018570:Risk Assessment
chemical_list: D002273:Carcinogens