View Item 

Show simple item record

Generalized monotonic functional mixed models with application to modelling normal tissue complications

dc.contributor.authorSchipper, Matthew J.en_US
dc.contributor.authorTaylor, Jeremy M. G.en_US
dc.contributor.authorLin, Xihongen_US
dc.date.accessioned2010-06-01T20:28:26Z
dc.date.available2010-06-01T20:28:26Z
dc.date.issued2008-04en_US
dc.identifier.citationSchipper, Matthew; Taylor, Jeremy M. G.; Lin, Xihong (2008). "Generalized monotonic functional mixed models with application to modelling normal tissue complications." Journal of the Royal Statistical Society: Series C (Applied Statistics) 57(2): 149-163. <http://hdl.handle.net/2027.42/73586>en_US
dc.identifier.issn0035-9254en_US
dc.identifier.issn1467-9876en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/73586
dc.format.extent747119 bytes
dc.format.extent3109 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherBlackwell Publishing Ltden_US
dc.rights© 2008 The Royal Statistical Society and Blackwell Publishing Ltden_US
dc.subject.otherDose Effecten_US
dc.subject.otherFunctional Dataen_US
dc.subject.otherMonotonicityen_US
dc.subject.otherNon-parametric Regressionen_US
dc.subject.otherNormal Tissue Complicationsen_US
dc.subject.otherOverdispersionen_US
dc.subject.otherSplinesen_US
dc.titleGeneralized monotonic functional mixed models with application to modelling normal tissue complicationsen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumUniversity of Michigan, Ann Arbor, USAen_US
dc.contributor.affiliationotherInnovative Analytics, Kalamazoo, USAen_US
dc.contributor.affiliationotherHarvard School of Public Health, Boston, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/73586/1/j.1467-9876.2007.00606.x.pdf
dc.identifier.doi10.1111/j.1467-9876.2007.00606.xen_US
dc.identifier.sourceJournal of the Royal Statistical Society: Series C (Applied Statistics)en_US
dc.identifier.citedreferenceBooth, J. G. and Hobert, J. P. ( 1998 ) Standard errors of prediction in generalized linear mixed models. J. Am. Statist. Ass., 93, 262 – 272.en_US
dc.identifier.citedreferenceBreslow, N. E. and Clayton, D. G. ( 1993 ) Approximate inference in generalized linear mixed models. J. Am. Statist. Ass., 88, 9 – 25.en_US
dc.identifier.citedreferenceDunson, D. B. ( 2005 ) Bayesian semiparametric isotonic regression for count data. J. Am. Statist. Ass., 100, 618 – 627.en_US
dc.identifier.citedreferenceEisbruch, A., TenHaken, R. K., Kim, H. M., Marsh, L. H. and Ship, O. ( 1999 ) Dose, volume, and function relationships in parotid salivary glands following conformal and intensity-modulated irradiation of head and neck cancer. Int. J. Radian. Oncol. Biol. Phys., 45, 577 – 587.en_US
dc.identifier.citedreferenceFriedman, J. and Tibshirani, R. ( 1984 ) The monotone smoothing of scatterplots. Technometrics, 26, 243 – 250.en_US
dc.identifier.citedreferenceGelfand, A. E. and Kuo, L. ( 1991 ) Nonparametric Bayesian bioassay including ordered polytomous response. Biometrika, 78, 657 – 666.en_US
dc.identifier.citedreferenceGreen, P. J. and Silverman, B. W. ( 1994 ) Nonparametric Regression and Generalized Linear Models. London: Chapman and Hall.en_US
dc.identifier.citedreferenceHall, P. and Huang, L. ( 2001 ) Nonparametric kernel regression subject to monotonicity constraints. Ann. Statist., 29, 624 – 647.en_US
dc.identifier.citedreferenceHolmes, C. C. and Heard, N. A. ( 2003 ) Generalised monotonic regression using random change points. Statist. Med., 22, 623 – 638.en_US
dc.identifier.citedreferenceJames, G. M. ( 2002 ) Generalized linear models with functional predictors. J. R. Statist. Soc. B, 64, 411 – 432.en_US
dc.identifier.citedreferenceJohnson, T. D., Taylor, J. M. G., TenHaken, R. K. and Eisbruch, A. ( 2005 ) A Bayesian mixture model relating dose to critical organs and functional complication in 3D conformal radiation therapy. Biostatistics, 6, 615 – 632.en_US
dc.identifier.citedreferenceKelly, C. and Rice, J. ( 1990 ) Monotone smoothing with application to dose-response curves and the assessment of synergism. Biometrics, 46, 1071 – 1085.en_US
dc.identifier.citedreferenceKutcher, G. J., Burman, C. and Brewster, L. ( 1989 ) Calculation of complication probability factors from non-uniform tissue irradiation: the effective volume method. Int. J. Radian Oncol. Biol. Phys., 16, 1623 – 1630.en_US
dc.identifier.citedreferenceLichter, A. S. ( 1991 ) Three-dimensional conformal radiation therapy: a testable hypothesis. Int. J. Radian Oncol. Biol. Phys., 21, 853 – 855.en_US
dc.identifier.citedreferenceLyman, J. T. ( 1985 ) Complication probability as assessed from dose-volume-histograms. Radian Res., 104, 513 – 519.en_US
dc.identifier.citedreferenceMcCullagh, P. and Nelder, J. A. ( 1989 ) Generalized Linear Models, 2nd edn. London: Chapman and Hall.en_US
dc.identifier.citedreferencePinheiro, J. C. and Bates, D. M. ( 1995 ) Approximations to the log-likelihood function in the non-linear mixed-effects model. J. Computnl Graph. Statist., 4, 12 – 35.en_US
dc.identifier.citedreferenceRamsay, J. O. ( 1988 ) Monotone regression splines in action. Statist. Sci., 3, 425 – 461.en_US
dc.identifier.citedreferenceRamsay, J. O. ( 1998 ) Estimating smooth monotone functions. J. R. Statist. Soc. B, 60, 365 – 375.en_US
dc.identifier.citedreferenceRamsay, J. O. and Silverman, B. W. ( 1997 ) Functional Data Analysis, pp. 157 – 177. New York. Springer.en_US
dc.identifier.citedreferenceRuppert, D., Wand, M. P. and Carroll, R. J. ( 2003 ) Semiparametric Regression. Cambridge: Cambridge University Press.en_US
dc.identifier.citedreferenceSchipper, M. J., Taylor, J. M. G. and Lin, X. ( 2007 ) Bayesian generalized monotonic functional mixed models for the effects of radiation dose histograms on normal tissue complications. Statist. Med., 26, 4643 – 4656.en_US
dc.identifier.citedreferenceVonesh, E. F., Chinchilli, V. M. and Pu, K. ( 1996 ) Goodness-of-fit in generalized nonlinear mixed models. Biometrics, 52, 572 – 587.en_US
dc.identifier.citedreferenceZhang, D., Lin, X. and Sowers, M. F. ( 2007 ) Two-stage functional mixed models for evaluating the effect of longitudinal covariate profiles on a scalar outcome. Biometrics, 63, 351–362.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
j.1467-9876.2007.00606.x.pdf
Size:
729.6KB
Format:
PDF
View/Open

Show simple item record

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.