Last data update: Apr 22, 2024. (Total: 46599 publications since 2009)
Records 1-12 (of 12 Records) |
Query Trace: Wheeler MW [original query] |
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An extended and unified modeling framework for benchmark dose estimation for both continuous and binary data
Aerts M , Wheeler MW , Abrahantes JC . Environmetrics 2020 31 (7) Protection and safety authorities recommend the use of model averaging to determine the benchmark dose approach as a scientifically more advanced method compared with the no-observed-adverse-effect-level approach for obtaining a reference point and deriving health-based guidance values. Model averaging however highly depends on the set of candidate dose–response models and such a set should be rich enough to ensure that a well-fitting model is included. The currently applied set of candidate models for continuous endpoints is typically limited to two models, the exponential and Hill model, and differs completely from the richer set of candidate models currently used for binary endpoints. The objective of this article is to propose a general and wide framework of dose response models, which can be applied both to continuous and binary endpoints and covers the current models for both type of endpoints. In combination with the bootstrap, this framework offers a unified approach to benchmark dose estimation. The methodology is illustrated using two data sets, one with a continuous and another with a binary endpoint. |
Updated population minimal eliciting dose distributions for use in risk assessment of 14 priority food allergens
Remington BC , Westerhout J , Meima MY , Blom WM , Kruizinga AG , Wheeler MW , Taylor SL , Houben GF , Baumert JL . Food Chem Toxicol 2020 139 111259 Food allergy and allergen management are important global public health issues. In 2011, the first iteration of our allergen threshold database (ATDB) was established based on individual NOAELs and LOAELs from oral food challenge in roughly 1750 allergic individuals. Population minimal eliciting dose (EDp) distributions based on this dataset were published for 11 allergenic foods in 2014. Systematic data collection has continued (2011-2018) and the dataset now contains over 3400 data points. The current study provides new and updated EDp values for 14 allergenic foods and incorporates a newly developed Stacked Model Averaging statistical method for interval-censored data. ED01 and ED05 values, the doses at which 1%, and respectively 5%, of the respective allergic population would be predicted to experience any objective allergic reaction were determined. The 14 allergenic foods were cashew, celery, egg, fish, hazelnut, lupine, milk, mustard, peanut, sesame, shrimp (for crustacean shellfish), soy, walnut, and wheat. Updated ED01 estimates ranged between 0.03mg for walnut protein and 26.2mg for shrimp protein. ED05 estimates ranged between 0.4mg for mustard protein and 280mg for shrimp protein. The ED01 and ED05 values presented here are valuable in the risk assessment and subsequent risk management of allergenic foods. |
Quantal risk assessment database: A database for exploring patterns in quantal dose-response data in risk assessment and its application to develop priors for Bayesian dose-response analysis
Wheeler MW , Piegorsch WW , Bailer AJ . Risk Anal 2018 39 (3) 616-629 Quantitative risk assessments for physical, chemical, biological, occupational, or environmental agents rely on scientific studies to support their conclusions. These studies often include relatively few observations, and, as a result, models used to characterize the risk may include large amounts of uncertainty. The motivation, development, and assessment of new methods for risk assessment is facilitated by the availability of a set of experimental studies that span a range of dose-response patterns that are observed in practice. We describe construction of such a historical database focusing on quantal data in chemical risk assessment, and we employ this database to develop priors in Bayesian analyses. The database is assembled from a variety of existing toxicological data sources and contains 733 separate quantal dose-response data sets. As an illustration of the database's use, prior distributions for individual model parameters in Bayesian dose-response analysis are constructed. Results indicate that including prior information based on curated historical data in quantitative risk assessments may help stabilize eventual point estimates, producing dose-response functions that are more stable and precisely estimated. These in turn produce potency estimates that share the same benefit. We are confident that quantitative risk analysts will find many other applications and issues to explore using this database. |
Bayesian additive adaptive basis tensor product models for modeling high dimensional surfaces: an application to high-throughput toxicity testing.
Wheeler MW . Biometrics 2018 75 (1) 193-201 Many modern datasets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective and well suited for characterizing a surface in two or three dimensions, but they may suffer from difficulties when representing higher dimensional surfaces. Motivated by high throughput toxicity testing where observed dose-response curves are cross sections of a surface defined by a chemical's structural properties, a model is developed to characterize this surface to predict untested chemicals' dose-responses. This manuscript proposes a novel approach that models the multidimensional surface as a sum of learned basis functions formed as the tensor product of lower dimensional functions, which are themselves representable by a basis expansion learned from the data. The model is described and a Gibbs sampling algorithm is proposed. The approach is investigated in a simulation study and through data taken from the US EPA's ToxCast high throughput toxicity testing platform. |
Bayesian local extremum splines
Wheeler MW , Dunson DB , Herring AH . Biometrika 2017 104 (4) 939-952 We consider shape-restricted nonparametric regression on a closed set X CR, where it is reasonable to assume that the function has no more than H local extrema interior to X. Following a Bayesian approach we develop a nonparametric prior over a novel class of local extremum splines. This approach is shown to be consistent when modelling any continuously differentiable function within the class considered, and we use it to develop methods for testing hypotheses on the shape of the curve. Sampling algorithms are developed, and the method is applied in simulation studies and data examples where the shape of the curve is of interest. |
Bayesian quantile impairment threshold benchmark dose estimation for continuous endpoints
Wheeler MW , Bailer AJ , Cole T , Park RM , Shao K . Risk Anal 2017 37 (11) 2107-2118 Quantitative risk assessment often begins with an estimate of the exposure or dose associated with a particular risk level from which exposure levels posing low risk to populations can be extrapolated. For continuous exposures, this value, the benchmark dose, is often defined by a specified increase (or decrease) from the median or mean response at no exposure. This method of calculating the benchmark dose does not take into account the response distribution and, consequently, cannot be interpreted based upon probability statements of the target population. We investigate quantile regression as an alternative to the use of the median or mean regression. By defining the dose-response quantile relationship and an impairment threshold, we specify a benchmark dose as the dose associated with a specified probability that the population will have a response equal to or more extreme than the specified impairment threshold. In addition, in an effort to minimize model uncertainty, we use Bayesian monotonic semiparametric regression to define the exposure-response quantile relationship, which gives the model flexibility to estimate the quantal dose-response function. We describe this methodology and apply it to both epidemiology and toxicology data. |
Bayesian hierarchical structure for quantifying population variability to inform probabilistic health risk assessments
Shao K , Allen BC , Wheeler MW . Risk Anal 2016 37 (10) 1865-1878 Human variability is a very important factor considered in human health risk assessment for protecting sensitive populations from chemical exposure. Traditionally, to account for this variability, an interhuman uncertainty factor is applied to lower the exposure limit. However, using a fixed uncertainty factor rather than probabilistically accounting for human variability can hardly support probabilistic risk assessment advocated by a number of researchers; new methods are needed to probabilistically quantify human population variability. We propose a Bayesian hierarchical model to quantify variability among different populations. This approach jointly characterizes the distribution of risk at background exposure and the sensitivity of response to exposure, which are commonly represented by model parameters. We demonstrate, through both an application to real data and a simulation study, that using the proposed hierarchical structure adequately characterizes variability across different populations. |
Non-chemical risk assessment for lifting and low back pain based on Bayesian threshold models
Pandalai SP , Wheeler MW , Lu M . Saf Health Work 2017 8 (2) 206-211 AbstractBackground Self-reported low back pain (LBP) has been evaluated in relation to material handling lifting tasks, but little research has focused on relating quantifiable stressors to LBP at the individual level. The National Institute for Occupational Safety and Health (NIOSH) Composite Lifting Index (CLI) has been used to quantify stressors for lifting tasks. A chemical exposure can be readily used as an exposure metric or stressor for chemical risk assessment (RA). Defining and quantifying lifting nonchemical stressors and related adverse responses is more difficult. Stressor–response models appropriate for CLI and LBP associations do not easily fit in common chemical RA modeling techniques (e.g., Benchmark Dose methods), so different approaches were tried. Methods This work used prospective data from 138 manufacturing workers to consider the linkage of the occupational stressor of material lifting to LBP. The final model used a Bayesian random threshold approach to estimate the probability of an increase in LBP as a threshold step function. Results Using maximal and mean CLI values, a significant increase in the probability of LBP for values above 1.5 was found. Conclusion A risk of LBP associated with CLI values > 1.5 existed in this worker population. The relevance for other populations requires further study. |
Historical context and recent advances in exposure-response estimation for deriving occupational exposure limits
Wheeler MW , Park RM , Bailer AJ , Whittaker C . J Occup Environ Hyg 2015 12 Suppl 1 0 Virtually no occupational exposure standards specify the level of risk for the prescribed exposure, and most occupational exposure limits are not based on quantitative risk assessment (QRA) at all. Wider use of QRA could improve understanding of occupational risks while increasing focus on identifying exposure concentrations conferring acceptably low levels of risk to workers. Exposure-response modeling between a defined hazard and the biological response of interest is necessary to provide a quantitative foundation for risk-based occupational exposure limits; and there has been considerable work devoted to establishing reliable methods quantifying the exposure-response relationship including methods of extrapolation below the observed responses. We review of several exposure-response modeling methods available for QRA, and demonstrate their utility with simulated data sets. |
Mechanistic hierarchical Gaussian processes
Wheeler MW , Dunson DB , Pandalai SP , Baker BA , Herring AH . J Am Stat Assoc 2014 109 (507) 894-904 The statistics literature on functional data analysis focuses primarily on flexible black-box approaches, which are designed to allow individual curves to have essentially any shape while characterizing variability. Such methods typically cannot incorporate mechanistic information, which is commonly expressed in terms of differential equations. Motivated by studies of muscle activation, we propose a nonparametric Bayesian approach that takes into account mechanistic understanding of muscle physiology. A novel class of hierarchical Gaussian processes is defined that favors curves consistent with differential equations defined on motor, damper, spring systems. A Gibbs sampler is proposed to sample from the posterior distribution and applied to a study of rats exposed to non-injurious muscle activation protocols. Although motivated by muscle force data, a parallel approach can be used to include mechanistic information in broad functional data analysis applications. |
An empirical comparison of low-dose extrapolation from points of departure (PoD) compared to extrapolations based upon methods that account for model uncertainty
Wheeler MW , Bailer AJ . Regul Toxicol Pharmacol 2013 67 (1) 75-82 Experiments with relatively high doses are often used to predict risks at appreciably lower doses.A point of departure (PoD) can be calculated as the dose associated with a specified moderate response level that is often in the range of experimental doses considered. A linear extrapolation to lower doses often follows.An alternative to the PoD method is to develop a model that accounts for the model uncertainty in the dose-response relationship and to use this model to estimate the risk at low doses.Two such approaches that account for model uncertainty are model averaging (MA) and semi-parametric methods.We use these methods, along with the PoD approach in the context of a large animal (40,000+ animal) bioassay that exhibited sub-linearity. When models are fit to high dose data and risks at low doses are predicted, the methods that account for model uncertainty produce dose estimates associated with an excess risk that are closer to the observed risk than the PoD linearization.This comparison provides empirical support to accompany previous simulation studies that suggest methods that incorporate model uncertainty provide viable, and arguably preferred, alternatives to linear extrapolation from a PoD. |
Contributions of dust exposure and cigarette smoking to emphysema severity in coal miners in the United States
Kuempel ED , Wheeler MW , Smith RJ , Vallyathan V , Green FH . Am J Respir Crit Care Med 2009 180 (3) 257-64 RATIONALE: Previous studies have shown associations between dust exposure or lung burden and emphysema in coal miners, although the separate contributions of various predictors have not been clearly demonstrated. OBJECTIVES: To quantitatively evaluate the relationship between cumulative exposure to respirable coal mine dust, cigarette smoking, and other factors on emphysema severity. METHODS: The study group included 722 autopsied coal miners and nonminers in the United States. Data on work history, smoking, race, and age at death were obtained from medical records and questionnaire completed by next-of-kin. Emphysema was classified and graded using a standardized schema. Job-specific mean concentrations of respirable coal mine dust were matched with work histories to estimate cumulative exposure. Relationships between various metrics of dust exposure (including cumulative exposure and lung dust burden) and emphysema severity were investigated in weighted least squares regression models. MEASUREMENTS AND MAIN RESULTS: Emphysema severity was significantly elevated in coal miners compared with nonminers among ever- and never-smokers (P < 0.0001). Cumulative exposure to respirable coal mine dust or coal dust retained in the lungs were significant predictors of emphysema severity (P < 0.0001) after accounting for cigarette smoking, age at death, and race. The contributions of coal mine dust exposure and cigarette smoking were similar in predicting emphysema severity averaged over this cohort. CONCLUSIONS: Coal dust exposure, cigarette smoking, age, and race are significant and additive predictors of emphysema severity in this study. |
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