Empirical Bayes calculations of concordance between endpoints in environmental toxicity experiments

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3 Citations (Scopus)

Abstract

Hierarchical models are considered for estimating the probability of agreement between two outcomes or endpoints from an environmental toxicity experiment. Emphasis is placed on generalized regression models, under which the prior mean is related to a linear combination of explanatory variables via a monotone function. This function defines the scale over which the systematic effects are modelled as additive. Specific illustration is provided for the logistic link function. The hierarchical model employs a conjugate beta prior that leads to parametric empirical Bayes estimators of the individual agreement parameters. An example from environmental carcinogenesis illustrates the methods, with motivation derived from estimation of the concordance between two species carcinogenicity outcomes. Based on a large database of carcinogenicity studies, the inter-species concordance is seen to be reasonably informative, i.e. in the range 67–84%. Stratification into pertinent potency-related sub-groups via the logistic model is seen to improve concordance estimation: for environmental stimuli at the extremes of the potency spectrum, concordance can reach well above 90%.

Original languageEnglish (US)
Pages (from-to)153-162
Number of pages10
JournalEnvironmental and Ecological Statistics
Volume1
Issue number2
DOIs
StatePublished - 1994
Externally publishedYes

Fingerprint

Empirical Bayes
Concordance
Toxicity
toxicity
Hierarchical Model
Experiment
logistics
experiment
Empirical Bayes Estimator
Link Function
Carcinogenesis
Logistic Model
Monotone Function
Stratification
Logistics
Linear Combination
Regression Model
Extremes
stratification
Subgroup

Keywords

  • agreement
  • confidence intervals
  • dichotomous data
  • environmental carcinogenesis
  • generalized linear models
  • hierarchical models
  • logistic regression
  • parametric empirical Bayes methods
  • potency
  • shrinkage estimator

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Environmental Science(all)
  • Statistics and Probability

Cite this

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title = "Empirical Bayes calculations of concordance between endpoints in environmental toxicity experiments",
abstract = "Hierarchical models are considered for estimating the probability of agreement between two outcomes or endpoints from an environmental toxicity experiment. Emphasis is placed on generalized regression models, under which the prior mean is related to a linear combination of explanatory variables via a monotone function. This function defines the scale over which the systematic effects are modelled as additive. Specific illustration is provided for the logistic link function. The hierarchical model employs a conjugate beta prior that leads to parametric empirical Bayes estimators of the individual agreement parameters. An example from environmental carcinogenesis illustrates the methods, with motivation derived from estimation of the concordance between two species carcinogenicity outcomes. Based on a large database of carcinogenicity studies, the inter-species concordance is seen to be reasonably informative, i.e. in the range 67–84{\%}. Stratification into pertinent potency-related sub-groups via the logistic model is seen to improve concordance estimation: for environmental stimuli at the extremes of the potency spectrum, concordance can reach well above 90{\%}.",
keywords = "agreement, confidence intervals, dichotomous data, environmental carcinogenesis, generalized linear models, hierarchical models, logistic regression, parametric empirical Bayes methods, potency, shrinkage estimator",
author = "Piegorsch, {Walter W}",
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journal = "Environmental and Ecological Statistics",
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