A Bayesian Network Model for Biomarker-Based Dose Response

Hack, C. Eric; Haber, Lynne T.; Maier, Andrew; Shulte, Paul; Fowler, Bruce A.; Lotz, W.; Savage, Russell E.

doi:10.1111/j.1539-6924.2010.01413.x

Cited by 27 publications

(17 citation statements)

References 45 publications

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“…Cross-sectional and/or longitudinal data, with selection biases and feedback among variables allowed Causal network, path analysis and structural equations models of change propagation [40] Do changes in exposures (or other causes) create a cascade of changes through a network of causal mechanisms (represented by equations), resulting in changes in the effect variables? Example: do relatively large variations in daily levels of fine particulate matter (PM2.5) air pollution create corresponding variations in markers of oxidative stress in the lungs?…”

Section: Time Series Data On Hypothesized Causes and Effectsmentioning

confidence: 99%

Has reducing fine particulate matter and ozone caused reduced mortality rates in the United States?

Cox

Popken

2015

Annals of Epidemiology

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Section: Time Series Data On Hypothesized Causes and Effectsmentioning

confidence: 99%

Has reducing fine particulate matter and ozone caused reduced mortality rates in the United States?

Cox

Popken

2015

Annals of Epidemiology

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“…If exposure causes adverse health effects, it must do so via one or more causal pathways. Collecting biomarker data can allow testing of specific causal hypotheses about the mechanisms of harm . Causal graph models, which factor joint distributions into marginal and conditional distributions, can be constructed to preserve causal orderings from structural equations or mathematical mechanistic models .…”

Section: How To Do Better: More Objective Tests For Causal Impactsmentioning

confidence: 99%

“…Collecting biomarker data can allow testing of specific causal hypotheses about the mechanisms of harm. (48) Causal graph models, which factor joint distributions into marginal and conditional distributions, (34) can be constructed to preserve causal orderings from structural equations or mathematical mechanistic models. (29,35,36) Then, if predicted changes in the variables that are supposed to transmit causal impacts are not observed, this would provide evidence against the hypothesized causal mechanism.…”

Section: How To Do Better: More Objective Tests For Causal Impactsmentioning

confidence: 99%

“…Causal network models of change propagation (35,48) Do changes in exposures (or other causes) create a cascade of changes through a network of causal mechanisms (represented by equations), resulting in changes in the effect variables? Example: Do relatively large variations in daily levels of fine particulate matter (PM2.5) air pollution create corresponding variations in markers of oxidative stress in the lungs?…”

Section: Time Series Data On Hypothesized Causes and Effectsmentioning

confidence: 99%

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Improving Causal Inferences in Risk Analysis

Cox¹

2013

Risk Analysis

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Recent headlines and scientific articles projecting significant human health benefits from changes in exposures too often depend on unvalidated subjective expert judgments and modeling assumptions, especially about the causal interpretation of statistical associations. Some of these assessments are demonstrably biased toward false positives and inflated effects estimates. More objective, data-driven methods of causal analysis are available to risk analysts. These can help to reduce bias and increase the credibility and realism of health effects risk assessments and causal claims. For example, quasi-experimental designs and analysis allow alternative (noncausal) explanations for associations to be tested, and refuted if appropriate. Panel data studies examine empirical relations between changes in hypothesized causes and effects. Intervention and change-point analyses identify effects (e.g., significant changes in health effects time series) and estimate their sizes. Granger causality tests, conditional independence tests, and counterfactual causality models test whether a hypothesized cause helps to predict its presumed effects, and quantify exposure-specific contributions to response rates in differently exposed groups, even in the presence of confounders. Causal graph models let causal mechanistic hypotheses be tested and refined using biomarker data. These methods can potentially revolutionize the study of exposure-induced health effects, helping to overcome pervasive false-positive biases and move the health risk assessment scientific community toward more accurate assessments of the impacts of exposures and interventions on public health.

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“…Another approach to biologically informed empirical dose–response modeling was demonstrated by Hack et al (2010), who used a Bayesian network model to integrate diverse types of data and conduct a biomarker-based exposure-dose–response assessment for benzene-induced acute myeloid leukemia (AML). The network approach was used to evaluate and compare individual biomarkers and quantitatively link the biomarkers along the exposure-disease continuum.…”

Section: Low-dose Extrapolation: Transition From Defaults To Mode Of mentioning

confidence: 99%

Advancing human health risk assessment: Integrating recent advisory committee recommendations

Dourson

Becker

Haber

et al. 2013

Critical Reviews in Toxicology

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Over the last dozen years, many national and international expert groups have considered specific improvements to risk assessment. Many of their stated recommendations are mutually supportive, but others appear conflicting, at least in an initial assessment. This review identifies areas of consensus and difference and recommends a practical, biology-centric course forward, which includes: (1) incorporating a clear problem formulation at the outset of the assessment with a level of complexity that is appropriate for informing the relevant risk management decision; (2) using toxicokinetics and toxicodynamic information to develop Chemical Specific Adjustment Factors (CSAF); (3) using mode of action (MOA) information and an understanding of the relevant biology as the key, central organizing principle for the risk assessment; (4) integrating MOA information into dose–response assessments using existing guidelines for non-cancer and cancer assessments; (5) using a tiered, iterative approach developed by the World Health Organization/International Programme on Chemical Safety (WHO/IPCS) as a scientifically robust, fit-for-purpose approach for risk assessment of combined exposures (chemical mixtures); and (6) applying all of this knowledge to enable interpretation of human biomonitoring data in a risk context. While scientifically based defaults will remain important and useful when data on CSAF or MOA to refine an assessment are absent or insufficient, assessments should always strive to use these data. The use of available 21st century knowledge of biological processes, clinical findings, chemical interactions, and dose–response at the molecular, cellular, organ and organism levels will minimize the need for extrapolation and reliance on default approaches.

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A Bayesian Network Model for Biomarker-Based Dose Response

Cited by 27 publications

References 45 publications

Has reducing fine particulate matter and ozone caused reduced mortality rates in the United States?

Has reducing fine particulate matter and ozone caused reduced mortality rates in the United States?

Improving Causal Inferences in Risk Analysis

Advancing human health risk assessment: Integrating recent advisory committee recommendations

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