Abstract
This paper describes the use of multiple models and model averaging for considering dose–response uncertainties when extrapolating low-dose risk from studies of populations with high levels of exposure. The model averaging approach we applied builds upon innovative methods developed by the U.S. Food and Drug Administration (FDA), principally through the relaxing of model constraints. The relaxing of model constraints allowed us to evaluate model uncertainty using a broader set of model forms and, within the context of model averaging, did not result in the extreme supralinearity that is the primary concern associated with the application of individual unconstrained models. A study of the relationship between inorganic arsenic exposure to a Taiwanese population and potential carcinogenic effects is used to illustrate the approach. We adjusted the reported number of cases from two published prospective cohort studies of bladder and lung cancer in a Taiwanese population to account for potential covariates and less-than-lifetime exposure (for estimating effects on lifetime cancer incidence), used bootstrap methods to estimate the uncertainty surrounding the µg/kg-day inorganic arsenic dose from drinking water and dietary intakes, and fit multiple models weighted by Bayesian Information Criterion to the adjusted incidence and dose data to generate dose-specific mean, 2.5th and 97.5th percentile risk estimates. Widely divergent results from adequate model fits for a broad set of constrained and unconstrained models applied individually and in a model averaging framework suggest that substantial model uncertainty exists in risk extrapolation from estimated doses in the Taiwanese studies to lower doses more relevant to countries like the U.S. that have proportionally lower arsenic intake levels.
Highlights
This paper describes an approach that involves the use of dose–response model averaging using multiple constrained and unconstrained models for evaluating model uncertainty
Published studies of the risk of bladder cancer (Chen et al, 2010b) and lung cancer (Chen et al, 2010a) within a large northeastern Taiwanese population that form the basis for the WHO (2011) and Food and Drug Administration (FDA) (FDA, 2016; Carrington et al, 2013) inorganic arsenic assessments are used to illustrate the approach
Inorganic arsenic concentrations in specific food items are assumed to be independent of water exposure levels; that is, while cooking water intake is modeled as a function of selected for calculating extra risks using the model averaging results from these studies was set to the estimated arsenic dose encountered by the general U.S drinking water concentration, inorganic arsenic concentrations in foods do not vary with modeled inorganic arsenic concentration
Summary
Published studies of the risk of bladder cancer (Chen et al, 2010b) and lung cancer (Chen et al, 2010a) within a large northeastern Taiwanese population that form the basis for the WHO (2011) and FDA (FDA, 2016; Carrington et al, 2013) inorganic arsenic assessments are used to illustrate the approach The suitability of these Taiwanese studies for dose– response analysis is uncertain due to questions around the ability to describe dose–response from levels of inorganic arsenic intake estimated for this population, 0.85 μg/kg-day for the reference groups and 2.0 μg/kg-day for the lowest exposure group (see Table 5) down to the below 0.1 μg/kg-day background levels of inorganic arsenic estimated for other populations such as the U.S (see Section 2.4). Overview of the Chen et al (2010b; 2010a) studies The prospective cohort studies of bladder (Chen et al, 2010b) and lung cancer risk (Chen et al, 2010a) in 8,086 adult residents, aged 40 years and older, residing in four townships in northeastern Taiwan afford several advantages that have enticed risk assessors (Carrington et al, 2013; FDA, 2016; WHO, 2011) to use them as the basis for prominent dose–response analyses:
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
