Please use this identifier to cite or link to this item: https://doi.org/10.1002/bimj.200710376
DC FieldValue
dc.titleEstimation and confidence regions for multi-dimensional effective dose
dc.contributor.authorLi, J.
dc.contributor.authorNordheim, E.V.
dc.contributor.authorZhang, C.
dc.contributor.authorLehner, C.E.
dc.date.accessioned2014-10-28T05:11:54Z
dc.date.available2014-10-28T05:11:54Z
dc.date.issued2008-02
dc.identifier.citationLi, J., Nordheim, E.V., Zhang, C., Lehner, C.E. (2008-02). Estimation and confidence regions for multi-dimensional effective dose. Biometrical Journal 50 (1) : 110-122. ScholarBank@NUS Repository. https://doi.org/10.1002/bimj.200710376
dc.identifier.issn03233847
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105129
dc.description.abstractThe problem of finding confidence regions for multiple predictor variables corresponding to given expected values of a response variable has not been adequately resolved. Motivated by an example from a study on hyperbaric exposure using a logistic regression model, we develop a conceptual framework for the estimation of the multi-dimensional effective dose for binary outcomes. The k-dimensional effective dose can be determined by conditioning on k - 1 components and solving for the last component as a conditional univariate effective dose. We consider various approaches for calculating confidence regions for the multi-dimensional effective dose and compare them via a simulation study for a range of possible designs. We analyze data related to decompression sickness to illustrate our procedure. Our results provide a practical approach to finding confidence regions for predictor variables for a given response value. © 2008 Wiley-VCH Verlag GmbH & Co. KGaA.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1002/bimj.200710376
dc.sourceScopus
dc.subjectBinary logistic regression
dc.subjectDecompression sickness
dc.subjectEffective dose
dc.subjectInverse inference
dc.subjectSimultaneous confidence regions
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1002/bimj.200710376
dc.description.sourcetitleBiometrical Journal
dc.description.volume50
dc.description.issue1
dc.description.page110-122
dc.identifier.isiut000253745200009
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