Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/1969
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dc.contributor.authorFAES, Christel-
dc.contributor.authorGEYS, Helena-
dc.contributor.authorAERTS, Marc-
dc.contributor.authorMOLENBERGHS, Geert-
dc.date.accessioned2007-11-09T15:17:10Z-
dc.date.available2007-11-09T15:17:10Z-
dc.date.issued2006-
dc.identifier.citationCOMPUTATIONAL STATISTICS & DATA ANALYSIS, 51(3). p. 1848-1861-
dc.identifier.issn0167-9473-
dc.identifier.urihttp://hdl.handle.net/1942/1969-
dc.description.abstractWithin the past decade, there has been an increasing interest in the problem of joint analysis of clustered multiple outcome data, motivated by developmental toxicity applications. Typically, a toxic insult early in gestation may result in a resorbed fetus or a fetal death. If however the fetus survives the entire gestation period, growth reduction such as low birth weight may occur, or can exhibit a malformation. Ideally, a model should take the complete hierarchical structure of the data into account. So far, however, one has tackled the challenges in this setting only partly each time making different restricting assumptions (e.g., restriction to viable fetuses only). In addition, because of genetic similarity and the same treatment conditions, offsprings of the same mother behave more alike than those of another mother. Thus, responses on different fetuses within a cluster are likely to be correlated. The ultimate scientific question requires assessing the full effect of dose administration, not only in the malformation and weight outcome, but also in the death outcome. A hierarchical Bayesian method is proposed to this effect. Such a model can serve as a basis for quantitative risk assessment. (c) 2005 Elsevier B.V. All rights reserved.-
dc.description.sponsorshipWe gratefully acknowledge support from the Institute for the Promotion of Innovation by Science and Technology (IWT) in Flanders, Belgium, from the Fund for Scientific Research Flanders and from the IAP research network nr P5/24 of the Belgian Government (Belgian Science Policy).-
dc.languageEnglish-
dc.language.isoen-
dc.publisherELSEVIER SCIENCE BV-
dc.rights(c) 2005 Elsevier B.V. All rights reserved.-
dc.subject.otherBayesian methods; benchmark dose; hierarchical model; toxicology-
dc.subject.otherBayesian methods; benchmark dose; hierarchical model; toxicology-
dc.titleA hierarchical modeling approach for risk assessment in developmental toxicity studies-
dc.typeJournal Contribution-
dc.identifier.epage1861-
dc.identifier.issue3-
dc.identifier.spage1848-
dc.identifier.volume51-
local.format.pages14-
local.bibliographicCitation.jcatA1-
dc.description.notesHasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium.Faes, C, Hasselt Univ, Ctr Stat, Agoralaan, B-3590 Diepenbeek, Belgium.christel.faes@uhasselt.be-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.bibliographicCitation.oldjcatA1-
dc.identifier.doi10.1016/j.csda.2005.12.002-
dc.identifier.isi000242704300030-
item.fulltextWith Fulltext-
item.accessRightsRestricted Access-
item.contributorMOLENBERGHS, Geert-
item.contributorGEYS, Helena-
item.contributorFAES, Christel-
item.contributorAERTS, Marc-
item.fullcitationFAES, Christel; GEYS, Helena; AERTS, Marc & MOLENBERGHS, Geert (2006) A hierarchical modeling approach for risk assessment in developmental toxicity studies. In: COMPUTATIONAL STATISTICS & DATA ANALYSIS, 51(3). p. 1848-1861.-
item.validationecoom 2008-
crisitem.journal.issn0167-9473-
crisitem.journal.eissn1872-7352-
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