[Experiments with radon and cigarette smoke]

Cancer models have been used for the analysis of incidence data in epidemiology and time to tumor data in experimental studies. The relevant quantities for the analyses of these data are the hazard function and the probability of tumor. We have begun to use these models for the analysis of data on intermediate lesions on the pathway to cancer. Such data are available in experimental carcinogenesis studies, in particular in initiation and promotion studies on the mouse skin and the rat liver. Typically, such data take the form of information on the number and size distribution of intermediate lesions as functions of the dose of the chemical agents applied and time on study. If, quantitative information on intermediate lesions on the pathway to lung cancer were to become available at some future date, the methods that we have developed for the analysis of initiation promotion experiments could easily be applied to the analysis of these lesion. The mathematical derivations here are couched in terms of a particular two-mutation model of carcinogenesis. Extension to models postulating more than two mutations is not always straightforward

Effects of exposure uncertainties in the TSCE model and application to the Colorado miners data.

The simulations in this paper show that exposure measurement error affects the parameter estimates of the biologically motivated two-stage clonal expansion (TSCE) model. For both Berkson and classical error models, we show that likelihood-based techniques of correction work reliably. For classical errors, the distribution of true exposures needs to be known or estimated in addition to the distribution of recorded exposures conditional on true exposures. Usually the exposure uncertainty biases the model parameters toward the null and underestimates the precision. But when several parameters are allowed to be dependent on exposure, e.g. initiation and promotion, then their relative importance is also influenced, and more complicated effects of exposure uncertainty can occur. The application part of this paper shows for two different types of Berkson errors that a recent analysis of the data for the Colorado plateau miners with the TSCE model is not changed substantially when correcting for such errors. Specifically, the conjectured promoting action of radon remains as the dominant radiation effect for explaining these data. The estimated promoting action of radon increases by a factor of up to 1.2 for the largest assumed exposure uncertainties

Response to the Commentary of Donald A. Pierce (Radiat. Res. 160, 718-723, 2003)

no abstrac

Validity of geographically modeled environmental exposure estimates

Geographic modeling is increasingly being used to estimate long-term environmental exposures in epidemiologic studies of chronic disease outcomes. However, without validation against measured environmental concentrations, personal exposure levels, or biologic doses, these models cannot be assumed a priori to be accurate. This article discusses three examples of epidemiologic associations involving exposures estimated using geographic modeling, and identifies important issues that affect geographically modeled exposure assessment in these areas. In air pollution epidemiology, geographic models of fine particulate matter levels have frequently been validated against measured environmental levels, but comparisons between ambient and personal exposure levels have shown only moderate correlations. Estimating exposure to magnetic fields by using geographically modeled distances is problematic because the error is larger at short distances, where field levels can vary substantially. Geographic models of environmental exposure to pesticides, including paraquat, have seldom been validated against environmental or personal levels, and validation studies have yielded inconsistent and typically modest results. In general, the exposure misclassification resulting from geographic models of environmental exposures can be differential and can result in bias away from the null even if non-differential. Therefore, geographic exposure models must be rigorously constructed and validated if they are to be relied upon to produce credible scientific results to inform epidemiologic research. To our knowledge, such models have not yet successfully predicted an association between an environmental exposure and a chronic disease outcome that has eventually been established as causal, and may not be capable of doing so in the absence of thorough validation. © 2014 Informa Healthcare USA, Inc