View Item 

Show simple item record

A C‐index for recurrent event data: Application to hospitalizations among dialysis patients
dc.contributor.authorKim, Sehee
dc.contributor.authorSchaubel, Douglas E.
dc.contributor.authorMcCullough, Keith P.
dc.date.accessioned2018-07-13T15:47:06Z
dc.date.available2019-08-01T19:53:23Zen
dc.date.issued2018-06
dc.identifier.citationKim, Sehee; Schaubel, Douglas E.; McCullough, Keith P. (2018). "A C‐index for recurrent event data: Application to hospitalizations among dialysis patients." Biometrics 74(2): 734-743.
dc.identifier.issn0006-341X
dc.identifier.issn1541-0420
dc.identifier.urihttps://hdl.handle.net/2027.42/144622
dc.publisherWiley Periodicals, Inc.
dc.subject.otherRecurrent events
dc.subject.otherWild bootstrap
dc.subject.otherProportional rates model
dc.subject.otherModel discrimination
dc.subject.otherC‐index
dc.titleA C‐index for recurrent event data: Application to hospitalizations among dialysis patients
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMathematics
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/144622/1/biom12761.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/144622/2/biom12761_am.pdf
dc.identifier.doi10.1111/biom.12761
dc.identifier.sourceBiometrics
dc.identifier.citedreferenceRao, P. S., Schaubel, D. E., Guidinger, M. K., Andreoni, K. A., Wolfe, R. A., Merion, R. M., Port, F. K., and Sung, R. S. ( 2009 ). A comprehensive risk quantification score for deceased donor kidneys: the kidney donor risk index. Transplantation 88, 231 – 236.
dc.identifier.citedreferenceHarrell, F. E., Lee, K. L., and Mark, D. B. ( 1996 ). Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 15, 361 – 387.
dc.identifier.citedreferenceHeagerty, P. J., Lumley, T., and Pepe, M. S. ( 2000 ). Time‐dependent roc curves for censored survival data and a diagnostic marker. Biometrics 56, 337 – 344.
dc.identifier.citedreferenceHeagerty, P. J. and Zheng, Y. ( 2005 ). Survival model predictive accuracy and roc curves. Biometrics 61, 92 – 105.
dc.identifier.citedreferenceHuang, C.‐Y. and Wang, M.‐C. ( 2004 ). Joint modeling and estimation for recurrent event processes and failure time data. Journal of the American Statistical Association 99, 1153 – 1165.
dc.identifier.citedreferenceLawless, J. F. and Nadeau, C. ( 1995 ). Some simple robust methods for the analysis of recurrent events. Technometrics 37, 158 – 168.
dc.identifier.citedreferenceLin, D., Wei, L., Yang, I., and Ying, Z. ( 2000 ). Semiparametric regression for the mean and rate functions of recurrent events. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 62, 711 – 730.
dc.identifier.citedreferenceLiu, L., Wolfe, R. A., and Huang, X. ( 2004 ). Shared frailty models for recurrent events and a terminal event. Biometrics 60, 747 – 756.
dc.identifier.citedreferenceMiloslavsky, M., Keleş, S., van der Laan, M. J., and Butler, S. ( 2004 ). Recurrent events analysis in the presence of time‐dependent covariates and dependent censoring. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 66, 239 – 257.
dc.identifier.citedreferenceMoskowitz, C. S. and Pepe, M. S. ( 2004 ). Quantifying and comparing the accuracy of binary biomarkers when predicting a failure time outcome. Statistics in medicine 23, 1555 – 1570.
dc.identifier.citedreferenceNolan, D. and Pollard, D. ( 1987 ). U‐processes: rates of convergence. The Annals of Statistics 15, 780 – 799.
dc.identifier.citedreferenceNolan, D. and Pollard, D. ( 1988 ). Functional limit theorems for U‐processes. The Annals of Probability 16, 1291 – 1298.
dc.identifier.citedreferenceRobinson, B., Bieber, B., Pisoni, R., and Port, F. ( 2012 ). Dialysis outcomes and practice patterns study (DOPPS): Its strengths, limitations, and role in informing practices and policies. Clinical Journal of the American Society of Nephrology 7, 1897 – 1905.
dc.identifier.citedreferenceSchaubel, D. E., Zeng, D., and Cai, J. ( 2006 ). A semiparametric additive rates model for recurrent event data. Lifetime Data Analysis 12, 389 – 406.
dc.identifier.citedreferenceSharma, P., Shu, X., Schaubel, D. E., Sung, R. S., and Magee, J. C. ( 2016 ). Propensity score‐based survival benefit of simultaneous liver‐kidney transplant over liver transplant alone for recipients with pretransplant renal dysfunction. Liver Transplantation 22, 71 – 79.
dc.identifier.citedreferenceUno, H., Cai, T., Pencina, M. J., D’Agostino, R. B., and Wei, L. ( 2011 ). On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine 30, 1105 – 1117.
dc.identifier.citedreferenceUno, H., Cai, T., Tian, L., and Wei, L. ( 2007 ). Evaluating prediction rules for t‐year survivors with censored regression models. Journal of the American Statistical Association 102, 527 – 537.
dc.identifier.citedreferenceWiesner, R., Edwards, E., Freeman, R., Harper, A., Kim, R., and Kamath, P., et al. ( 2003 ). Model for end‐stage liver disease (MELD) and allocation of donor livers. Gastroenterology 124, 91 – 96.
dc.identifier.citedreferenceYe, Y., Kalbfleisch, J. D., and Schaubel, D. E. ( 2007 ). Semiparametric analysis of correlated recurrent and terminal events. Biometrics 63, 78 – 87.
dc.identifier.citedreferenceYoung, E. W., Goodkin, D. A., Mapes, D. L., Port, F. K., Keen, M. L., Chen, K., Maroni, B. L., Wolfe, R. A., and Held, P. J. ( 2000 ). The Dialysis Outcomes and Practice Patterns Study (DOPPS): An international hemodialysis study. Kidney International 57, 74 – 81.
dc.identifier.citedreferenceAndersen, P. K. and Gill, R. D. ( 1982 ). Cox’s regression model for counting processes: a large sample study. The Annals of Statistics 10, 1100 – 1120.
dc.identifier.citedreferenceCook, R. J. and Lawless, J. F. ( 2007 ). The Statistical Analysis of Recurrent Events. New Your, NY:Springer Science & Business Media.
dc.identifier.citedreferenceGail, M. H. and Mai, P. L. ( 2010 ). Comparing breast cancer risk assessment models. Journal of the National Cancer Institute 102, 665 – 668.
dc.identifier.citedreferenceGerds, T. A., Kattan, M. W., Schumacher, M., and Yu, C. ( 2013 ). Estimating a time‐dependent concordance index for survival prediction models with covariate dependent censoring. Statistics in Medicine 32, 2173 – 2184.
dc.identifier.citedreferenceGhosh, D. and Lin, D. ( 2002 ). Marginal regression models for recurrent and terminal events. Statistica Sinica 12, 663 – 688.
dc.identifier.citedreferenceHarrell, F. E., Califf, R. M., Pryor, D. B., Lee, K. L., and Rosati, R. A. ( 1982 ). Evaluating the yield of medical tests. Journal of the American Medical Association 247, 2543 – 2546.
dc.identifier.citedreferenceHarrell, F. E., Lee, K. L., Califf, R. M., Pryor, D. B., and Rosati, R. A. ( 1984 ). Regression modelling strategies for improved prognostic prediction. Statistics in Medicine 3, 143 – 152.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
biom12761.pdf
Size:
193.9KB
Format:
PDF
View/Open
Name:
biom12761_am.pdf
Size:
229.8KB
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.