Flexible quantitative risk assessment for developmental toxicity based on fractional polynomial predictors

Geys, H; Molenberghs, G; Declerck, L; Ryan, L

Flexible quantitative risk assessment for developmental toxicity based on fractional polynomial predictors

Geys, H Molenberghs, G Declerck, L Ryan, L

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Publication Type:: Journal Article
Citation:: Biometrical Journal, 2000, 42 (3), pp. 279 - 302
Issue Date:: 2000-01-01

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Field	Value	Language
dc.contributor.author	Geys, H	en_US
dc.contributor.author	Molenberghs, G	en_US
dc.contributor.author	Declerck, L	en_US
dc.contributor.author	Ryan, L https://orcid.org/0000-0001-5957-2490	en_US
dc.date.issued	2000-01-01	en_US
dc.identifier.citation	Biometrical Journal, 2000, 42 (3), pp. 279 - 302	en_US
dc.identifier.issn	0323-3847	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26255
dc.description.abstract	Risk assessment for developmental toxicity studies in rodents is faced with the fairly involved data structure of clustered multivariate binary outcomes. While likelihood methods for this setting do not abound, we show that a conditional model, combined with pseudo-likelihood inference and fractional polynomial predictor functions, as proposed by ROYSTON and ALTMAN (1994), are a promising way forward. The methods are illustrated using teratology data collected under the National Toxicology Program.	en_US
dc.relation.ispartof	Biometrical Journal	en_US
dc.relation.isbasedon	10.1002/1521-4036(200007)42:3<279::AID-BIMJ279>3.0.CO;2-F	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Flexible quantitative risk assessment for developmental toxicity based on fractional polynomial predictors	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	42	en_US
utslib.for	010402 Biostatistics	en_US
utslib.for	0104 Statistics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	42	en_US

Abstract:

Risk assessment for developmental toxicity studies in rodents is faced with the fairly involved data structure of clustered multivariate binary outcomes. While likelihood methods for this setting do not abound, we show that a conditional model, combined with pseudo-likelihood inference and fractional polynomial predictor functions, as proposed by ROYSTON and ALTMAN (1994), are a promising way forward. The methods are illustrated using teratology data collected under the National Toxicology Program.

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http://hdl.handle.net/10453/26255