Two-Stage Design of Quanta1 Response Studies
dc.contributor.author | Sitter, R. R. | en_US |
dc.contributor.author | Wu, C. F. J. | en_US |
dc.date.accessioned | 2010-04-01T14:43:48Z | |
dc.date.available | 2010-04-01T14:43:48Z | |
dc.date.issued | 1999-06 | en_US |
dc.identifier.citation | Sitter, R. R.; Wu, C. F. J. (1999). "Two-Stage Design of Quanta1 Response Studies." Biometrics 55(2): 396-402. <http://hdl.handle.net/2027.42/65175> | en_US |
dc.identifier.issn | 0006-341X | en_US |
dc.identifier.issn | 1541-0420 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/65175 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=11318192&dopt=citation | en_US |
dc.description.abstract | In a quantal response study, there may be insufficient knowledge of the response relationship for the stimulus (or dose) levels to be chosen properly. Information from such a study can be scanty or even unreliable. A two-stage design is proposed for such studies, which can determine whether and how a follow-up (i.e., second-stage) study should be conducted to select additional stimulus levels to compensate for the scarcity of information in the initial study. These levels are determined by using optimal design theory and are based on the fitted model from the data in the initial study. Its advantages are demonstrated using a fishery study. | en_US |
dc.format.extent | 772317 bytes | |
dc.format.extent | 3110 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | Blackwell Publishing Ltd | en_US |
dc.rights | The International Biometric society, 1999 | en_US |
dc.subject.other | Binary Data | en_US |
dc.subject.other | C -Optimality | en_US |
dc.subject.other | D -Optimality | en_US |
dc.subject.other | F -Optimality | en_US |
dc.subject.other | Logit | en_US |
dc.subject.other | Phase II Trials | en_US |
dc.subject.other | Probit | en_US |
dc.title | Two-Stage Design of Quanta1 Response Studies | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109-1027, U.S.A. | en_US |
dc.contributor.affiliationother | Department of Mathematics and Statistics, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada email: sitter@cs.sfu.ca | en_US |
dc.identifier.pmid | 11318192 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/65175/1/j.0006-341X.1999.00396.x.pdf | |
dc.identifier.doi | 10.1111/j.0006-341X.1999.00396.x | en_US |
dc.identifier.source | Biometrics | en_US |
dc.identifier.citedreference | Abdelbasit, K. M. and Plackett, R. L. ( 1983 ). Experimental design for binary data. Journal of the American Statistical Association 78, 90 – 98. | en_US |
dc.identifier.citedreference | Box, G. E. P., Hunter, W. G., and Hunter, J. S. ( 1978 ). Statistics for Experimenters, An Introduction to Design, Data Analysis, and Model Building. New York : John Wiley. | en_US |
dc.identifier.citedreference | Chaloner, K. and Larntz, K. ( 1989 ). Optimal Bayesian design applied to logistic regression experiments. Journal of Statistical Planning and Inference 21, 191 – 208. | en_US |
dc.identifier.citedreference | Finney, D. J. ( 1971 ) Probit Analysis, 3rd edition. Cambridge : The Cambridge University Press. | en_US |
dc.identifier.citedreference | McDonald, M. ( 1993 ) Dose-ranging studies: The key to registration. Applied Clinical Trials 2, 50 – 58. | en_US |
dc.identifier.citedreference | Minkin, S. ( 1987 ) Optimal design for binary data. Journal of the American Statistical Association 82, 1098 – 1103. | en_US |
dc.identifier.citedreference | Sitter, R. R. ( 1992 ) Robust designs for binary data. Biometrics 48, 1145 – 1156. | en_US |
dc.identifier.citedreference | Sitter, R. R. and Fainaru, I. ( 1997 ). Optimal designs for the logit and probit models for binary data. Canadian Journal of Statistics 25, 175 – 189. | en_US |
dc.identifier.citedreference | Sitter, R. R. and Forbes, B. ( 1997 ). Optimal two-stage designs for binary response experiments. Statistica Sinica 7, 941 – 956. | en_US |
dc.identifier.citedreference | Sitter, R. R. and Wu, C. F. J. ( 1993 ). Optimal designs for binary response experiments: Fieller, D, and A criteria. Scandinavian Journal of Statistics 20, 329 – 342. | en_US |
dc.identifier.citedreference | Sitter, R. R. and Wu, C. F. J. ( 1998 ). Two-stage design of quantal response studies. Research Report 98–1, Department of Mathematics and Statistics, Simon Fraser University, Burnaby, British Columbia, Canada. | en_US |
dc.identifier.citedreference | Wu, C. F. J. ( 1985 ). Efficient sequential designs with binary data. Journal of the American Statistical Association 80, 974 – 984. | en_US |
dc.identifier.citedreference | Wu, C. F. J. ( 1988 ). Optimal design for percentile estimation of a quantal response curve. In Optimal Design and Analysis of Experiments. Y. Dodge, V. Fedorov, and H. P. Wynn ( eds ), 213 – 223. Amsterdam : Elsevier Science Publishers B.V. ( North Holland ). | en_US |
dc.identifier.citedreference | Young, L. J. and Easterling, R. G. ( 1994 ). Estimation of extreme quantiles based on sensitivity tests: A comparative study. Technometrics 36, 48 – 60. | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.