A Generalized Multihit Dose-Response Model for Low-Dose Extrapolation

Rai, Kamta; Ryzin, John Van

doi:10.2307/2530422

Cited by 53 publications

(8 citation statements)

References 10 publications

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“…Alternative models can be formulated by assuming dierent distributions for the underlying variables. For example, a novel multihit model (Rai and Van Ryzin, 1981;Chu and Kuo, 1997) could be developed for multiple binary outcomes by assuming a Poisson distribution for the underlying variables:…”

Section: Clustered Multiple Binary Outcomesmentioning

confidence: 99%

Bayesian Latent Variable Models for Clustered Mixed Outcomes

Dunson

2000

Journal of the Royal Statistical Society Series B: Statistical Methodology

248

214

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A general framework is proposed for modelling clustered mixed outcomes. A mixture of generalized linear models is used to describe the joint distribution of a set of underlying variables, and an arbitrary function relates the underlying variables to the observed outcomes. The model accommodates multilevel data structures, general covariate effects and distinct link functions and error distributions for each underlying variable. Within the framework proposed, novel models are developed for clustered multiple binary, unordered categorical and joint discrete and continuous outcomes. A Markov chain Monte Carlo sampling algorithm is described for estimating the posterior distributions of the parameters and latent variables. Because of the¯exibility of the modelling framework and estimation procedure, extensions to ordered categorical outcomes and more complex data structures are straightforward. The methods are illustrated by using data from a reproductive toxicity study.

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Section: Clustered Multiple Binary Outcomesmentioning

confidence: 99%

Bayesian Latent Variable Models for Clustered Mixed Outcomes

Dunson

2000

Journal of the Royal Statistical Society Series B: Statistical Methodology

248

214

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“…29 where h(x) = xr -1 and f(d) is a polynomial (30) in dose rate] then the incidence with a distribution g(v) for latency periods will be rt I (d s t) / f (s)9 (r)h(tr)dr o =f (¢G(t) (31) where G(t) will depend on the parameters describing g(X) and thus may be estimated from data. Eq.…”

Section: (R T) F (D)h(t -)mentioning

confidence: 99%

Dose-Response Relationships for Carcinogens: A Review

Zeise¹,

Wilson²,

Crouch³

1987

Environmental Health Perspectives

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. The National Institute of Environmental Health Sciences (NIEHS) and Brogan & Partners are collaborating with JSTOR to digitize, preserve and extend access to Environmental Health Perspectives.We review the experimental evidence for various shapes of dose-response relationships for carcinogens and summarize those experiments that give the most information on relatively low doses. A brief review of some models is given to illustrate the shapes of dose-response curve expected from them. Our major interest is in the use of dose-response relationships to estimate risks to humans at low doses, and so we pay special attention to experimentally observed and theoretically expected nonlinearities. There are few experimental examples of nonlinear dose-response relations in humans, but this may simply be due to the limitations in the data. The several examples in rodents, even though for high dose data, suggest that nonlinearity is common. In some cases such nonlinearities may be rationalized on the basis of the pharmacokinetics of the test compound or its metabolites. mulae"). Of special interest are the observed nonlinearities in dose-response relationships, and the possible explanations for them.Most models of the cancer process are constructed by describing (mathematically) a set of elementary biological processes which are supposed to be fundamental. The effect of doses of carcinogens on these elementary processes is generally assumed to be the simplest possible (e.g., described by a chemical reaction rate), but the dose-response relationship for the whole model will usually be as arbitrary as the assumptions made for these elementary processes. Many of the mathematical models suggested are expressions of the biological idea of a multistage process. We demonstrate how many different dose-response formulae can be obtained within this one framework, although such formulae might also arise from alternative theoretical frameworks. Consequently, to avoid confusion, it seems preferable to refer to currently used dose-response relationships by some descriptor of the mathematical formulae, rather than by a descriptor of the particular theory or model used to justify them. "Human Studies" and "Animal Studies" summarize those experimental observations on cancer induction which show most clearly the shapes of dose-response curves. Epidemiological studies and bioassays can use too few subjects to give direct information about doses at which excess tumor rates are below about 1%. Where large numbers of people or animals have been exposed to various levels of a carcinogen we obtain the closest approach to low doses. In addition, studying the outcomes of a large number of small experiments can give P...

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“…An alternative to direct specification of a threshold distribution is the class of "hit theory" models [22,23]. These models postulate target locations in the subjects of interest, which, when hit by an insult or stimulus, may cause a failure response such as carcinogenesis.…”

Section: Reliability Vs Safety Testingmentioning

confidence: 99%

Panning for gold: Enhancing the precision of sensitivity test data

Collins

Weaver

Hamada

2017

Statistical Analysis

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Sensitivity tests apply a range of stimulus values to experimental subjects and record binary responses in order to estimate the distribution of threshold values in the subject population, where thresholds delineate responses from nonresponses. In many applications, such as explosives engineering, individual tests are expensive and are conducted in small runs. Scarcity of data results in nonexistence of estimates, or estimates with low precision. We discuss various methods, such as combining test runs, covariate analysis, and penalized maximum likelihood, for enhancing precision and “mining more gold” from expensive test results.

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A Generalized Multihit Dose-Response Model for Low-Dose Extrapolation

Cited by 53 publications

References 10 publications

Bayesian Latent Variable Models for Clustered Mixed Outcomes

Bayesian Latent Variable Models for Clustered Mixed Outcomes

Dose-Response Relationships for Carcinogens: A Review

Panning for gold: Enhancing the precision of sensitivity test data

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