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Inference for Constrained Estimation of Tumor Size Distributions

dc.contributor.authorGhosh, Debashisen_US
dc.contributor.authorBanerjee, Moulinathen_US
dc.contributor.authorBiswas, Pinakien_US
dc.date.accessioned2010-04-01T15:04:24Z
dc.date.available2010-04-01T15:04:24Z
dc.date.issued2008-12en_US
dc.identifier.citationGhosh, Debashis; Banerjee, Moulinath; Biswas, Pinaki (2008). "Inference for Constrained Estimation of Tumor Size Distributions." Biometrics 64(4): 1009-1017. <http://hdl.handle.net/2027.42/65536>en_US
dc.identifier.issn0006-341Xen_US
dc.identifier.issn1541-0420en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/65536
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=18371123&dopt=citationen_US
dc.description.abstractIn order to develop better treatment and screening programs for cancer prevention programs, it is important to be able to understand the natural history of the disease and what factors affect its progression. We focus on a particular framework first outlined by Kimmel and Flehinger (1991, Biometrics , 47, 987–1004) and in particular one of their limiting scenarios for analysis. Using an equivalence with a binary regression model, we characterize the nonparametric maximum likelihood estimation procedure for estimation of the tumor size distribution function and give associated asymptotic results. Extensions to semiparametric models and missing data are also described. Application to data from two cancer studies is used to illustrate the finite-sample behavior of the procedure.en_US
dc.format.extent379236 bytes
dc.format.extent3110 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherBlackwell Publishing Incen_US
dc.rights©2008 International Biometric Societyen_US
dc.subject.otherIsotonic Regressionen_US
dc.subject.otherOncologyen_US
dc.subject.otherPool-adjacent Violators Algorithmen_US
dc.subject.otherProfile Likelihooden_US
dc.subject.otherSemiparametric Information Bounden_US
dc.subject.otherSmoothing Splinesen_US
dc.titleInference for Constrained Estimation of Tumor Size Distributionsen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Statistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.en_US
dc.contributor.affiliationotherDepartment of Statistics and Huck Institute of Life Sciences, Penn State University, University Park, Pennsylvania 16802, U.S.A.en_US
dc.contributor.affiliationotherPfizer, New York, New York 10017, U.S.A.en_US
dc.identifier.pmid18371123en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/65536/1/j.1541-0420.2008.01001.x.pdf
dc.identifier.doi10.1111/j.1541-0420.2008.01001.xen_US
dc.identifier.sourceBiometricsen_US
dc.identifier.citedreferenceAmersi, F. and Hansen, N. M. ( 2006 ). The benefits and limitations of sentinel lymph node biopsy. Current Treatments and Options in Oncology 7, 141 – 151.en_US
dc.identifier.citedreferenceAyer, M., Brunk, H. D., Ewing, G. M., Reid, W. T., and Silverman, E. ( 1955 ). An empirical distribution function for sampling with incomplete information. Annals of Mathematical Statistics 26, 641 – 647.en_US
dc.identifier.citedreferenceBanerjee, M. and Wellner, J. A. ( 2001 ). Likelihood ratio tests for monotone functions. Annals of Statistics 29, 1699 – 1731.en_US
dc.identifier.citedreferenceDelgado, M. A., Rodriguez-Poo, J. M., and Wolf, M. ( 2001 ). Subsampling inference in cube root asymptotics with an application to Manski's maximum score estimator. Economics Letters 73, 241 – 250.en_US
dc.identifier.citedreferenceGhosh, D. ( 2006 ). Modelling tumor biology-progression relationships in screening trials. Statistics in Medicine 25, 1872 – 1884.en_US
dc.identifier.citedreferenceGhosh, D. ( 2008 ). Proportional hazards regression for cancer studies. Biometrics 64, to appear.en_US
dc.identifier.citedreferenceGroeneboom, P. and Wellner, J. A. ( 1992 ). Information Bounds and Nonparametric Maximum Likelihood Estimation. Boston : BirkhÄuser.en_US
dc.identifier.citedreferenceGroeneboom, P. and Wellner, J. A. ( 2001 ). Computing Chernoff's distribution. Journal of Computational and Graphical Statistics 10, 388 – 400.en_US
dc.identifier.citedreferenceHeckman, N. E. and Ramsay, J. O. ( 2000 ). Penalized regression with model-based penalties. Canadian Journal of Statistics 28, 241 – 258.en_US
dc.identifier.citedreferenceJewell, N. P. and van der Laan, M. J. ( 2004 ). Case-control current status data. Biometrika 91, 529 – 541.en_US
dc.identifier.citedreferenceKeiding, N., Begtrup, K., Scheike, T. H., and Hasibeder, G. ( 1996 ). Estimation from current-status data in continuous time. Lifetime Data Analysis 2, 119 – 129.en_US
dc.identifier.citedreferenceKimmel, M. and Flehinger, B. J. ( 1991 ). Nonparametric estimation of the size-metastasis relationship in solid cancers. Biometrics 47, 987 – 1004.en_US
dc.identifier.citedreferenceLehmann, E. L. ( 1999 ). Elements of Large-Sample Theory. New York : Springer.en_US
dc.identifier.citedreferenceMurphy, S. A. and van der Vaart, A. W. ( 1997 ). Semiparametric likelihood ratio inference. Annals of Statistics 25, 1471 – 1509.en_US
dc.identifier.citedreferenceMurphy, S. A. and van der Vaart, A. W. ( 2000 ). On profile likelihood (with discussion). Journal of the American Statistical Association 95, 449 – 485.en_US
dc.identifier.citedreferencePolitis, D. N., Romano, J. P., and Wolf, M. ( 1999 ). Subsampling. New York : Springer-Verlag.en_US
dc.identifier.citedreferenceRamsay, J. O. ( 1998 ). Estimating smooth monotone functions. Journal of the Royal Statistical Society, Series B 68, 365 – 375.en_US
dc.identifier.citedreferenceRobertson T., Wright F., and Dykstra R. ( 1988 ). Order Restricted Statistical Inference. New York : Wiley.en_US
dc.identifier.citedreferenceScott, A. J. and Wild, C. J.. ( 1997 ). Fitting regression models to case–control data by maximum likelihood. Biometrika 84, 57 – 71.en_US
dc.identifier.citedreferenceStaniswalis, J. G. and Thall, P. F. ( 2001 ). An explanation of generalized profile likelihoods. Statistics and Computing 11, 293 – 298.en_US
dc.identifier.citedreferenceVerschraegen, C., Vinh-Hung, V., Cserni, G., Gordon, R., Royce, M. E., Vlastos, G., Tai, P., and Storme, G. ( 2005 ). Modeling the effect of tumor size in early breast cancer. Annals of Surgery 241, 309 – 318.en_US
dc.identifier.citedreferenceWahba, G. ( 1983 ). Bayesian confidence intervals for the cross validated smoothing spline. Journal of the Royal Statistical Society, Series B 45, 133 – 150.en_US
dc.identifier.citedreferenceXu, J. L. and Prorok, P. C. ( 1997 ). Nonparametric estimation of solid cancer size at metastasis and probability of presenting with metastasis at detection. Biometrics 53, 579 – 591.en_US
dc.identifier.citedreferenceXu, J. L. and Prorok, P. C. ( 1998 ). Estimating a distribution function of the tumor size at metastasis. Biometrics 54, 859 – 864.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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