Information‐theoretic model‐averaged benchmark dose analysis in environmental risk assessment

Piegorsch, Walter W.; An, Lingling; Wickens, Alissa A.; West, R. Webster; Peña, Edsel A.; Wu, Wensong

doi:10.1002/env.2201

Cited by 33 publications

(36 citation statements)

References 46 publications

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“…Various investigations have applied the Akaike information criterion (AIC) to calculate the weight for model averaging of BMD values . The weights (Pr) for model M k with respect to data set D can be calculated by:

Pr (M_{k} | D) = \frac{\exp (- 0.5 A I C_{k})}{\sum_{t = 1}^{k} \exp (- 0.5 A I C_{t})}

where

A I C_{k} = - 2 L_{k} + 2 p_{k}

…”

Section: Methodsmentioning

confidence: 99%

Influence of Distribution of Animals between Dose Groups on Estimated Benchmark Dose and Animal Distress for Quantal Responses

et al. 2017

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Increasingly, dose-response data are being evaluated with the benchmark dose (BMD) approach rather than by the less precise no-observed-adverse-effect-level (NOAEL) approach. However, the basis for designing animal experiments, using equally sized dose groups, is still primed for the NOAEL approach. The major objective here was to assess the impact of using dose groups of unequal size on both the quality of the BMD and overall animal distress. We examined study designs with a total number of 200 animals distributed in four dose groups employing quantal data generated by Monte Carlo simulations. Placing more animals at doses close to the targeted BMD provided an estimate of BMD that was slightly better than the standard design with equally sized dose groups. In situations involving a clear dose-response, this translates into fewer animals receiving high doses and thus less overall animal distress. Accordingly, in connection with risk and safety assessment, animal distress can potentially be reduced by distributing the animals appropriately between dose groups without decreasing the quality of the information obtained.

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Pr (M_{k} | D) = \frac{\exp (- 0.5 A I C_{k})}{\sum_{t = 1}^{k} \exp (- 0.5 A I C_{t})}

where

A I C_{k} = - 2 L_{k} + 2 p_{k}

…”

Section: Methodsmentioning

confidence: 99%

Influence of Distribution of Animals between Dose Groups on Estimated Benchmark Dose and Animal Distress for Quantal Responses

et al. 2017

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“…In fact, model selection based on the popular Akaike information criteria (AIC) (Akaike, 1973) has been shown to select incorrect models for BMD estimation almost as often as it selects correct models (West et al, 2012). Evidence is growing that naïve model selection prior to BMD estimation can lead to wildly incorrect BMDLs (Ringblom et al, 2014); by contrast, use of formal model averaging can mitigate the problems associated with such single-stand model selection tactics (Piegorsch et al, 2013).…”

Section: Bayesian Model Averagingmentioning

confidence: 99%

“…To adjust, we explore a Bayesian model averaging (BMA) framework for the estimation process (Hoeting et al, ). Previous parametric model averaging schemes for BMDs usually focused on at most three or four different forms, although some writers have expanded to as many as eight different dose‐response functions (Wheeler and Bailer, ; West et al, ; Piegorsch et al, ). These correspond to popular choices in the U.S. EPA's BMDS software (Davis et al, ), and are cataloged in Table .…”

Section: Introductionmentioning

confidence: 99%

Bayesian Model-Averaged Benchmark Dose Analysis via Reparameterized Quantal-Response Models

Fang¹,

Piegorsch

Simmons

et al. 2015

Biometrics

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An important objective in biomedical risk assessment is estimation of minimum exposure levels that induce a pre-specified adverse response in a target population. The exposure/dose points in such settings are known as Benchmark Doses (BMDs). Recently, parametric Bayesian estimation for finding BMDs has become popular. A large variety of candidate dose-response models is available for applying these methods, however, leading to questions of model adequacy and uncertainty. Here we enhance the Bayesian estimation technique for BMD analysis by applying Bayesian model averaging to produce point estimates and (lower) credible bounds. We include reparameterizations of traditional dose-response models that allow for more-focused use of elicited prior information when building the Bayesian hierarchy. Performance of the method is evaluated via a short simulation study. An example from carcinogenicity testing illustrates the calculations.

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“…Specifically, we have

p_{i}^{+} = P γ_{i} = 1 | y_{i}, f_{i 0}, f_{i 1}, π_{i} = π_{i} f_{i 1} y_{i} (1 - π_{i}) f_{i 0} (y_{i}) + π_{i} f_{i 1} (y_{i}) .

These quantities offer an interpretable and unequivocal scale of evidence in the classification of potentially hazardous nano‐materials. From a modeling perspective, the limitations of standard dose–response analysis and the importance of mixture modeling ideas have a been recognized by several authors (Boos and Brownie, ; Piegorsch et al ), even though, to our knowledge, we are the first to apply this strategy in the joint analysis of multiple biological stressors, specifically nano‐materials.…”

Section: Model Formulationmentioning

confidence: 99%

Relating nano‐particle properties to biological outcomes in exposure escalation experiments

et al. 2013

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A fundamental goal in nano-toxicology is that of identifying particle physical and chemical properties, which are likely to explain biological hazard. The first line of screening for potentially adverse outcomes often consists of exposure escalation experiments, involving the exposure of micro-organisms or cell lines to a library of nanomaterials. We discuss a modeling strategy, that relates the outcome of an exposure escalation experiment to nanoparticle properties. Our approach makes use of a hierarchical decision process, where we jointly identify particles that initiate adverse biological outcomes and explain the probability of this event in terms of the particle physicochemical descriptors. The proposed inferential framework results in summaries that are easily interpretable as simple probability statements. We present the application of the proposed method to a data set on 24 metal oxides nanoparticles, characterized in relation to their electrical, crystal and dissolution properties.

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Information‐theoretic model‐averaged benchmark dose analysis in environmental risk assessment

Cited by 33 publications

References 46 publications

Influence of Distribution of Animals between Dose Groups on Estimated Benchmark Dose and Animal Distress for Quantal Responses

Influence of Distribution of Animals between Dose Groups on Estimated Benchmark Dose and Animal Distress for Quantal Responses

Bayesian Model-Averaged Benchmark Dose Analysis via Reparameterized Quantal-Response Models

Relating nano‐particle properties to biological outcomes in exposure escalation experiments

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