View Item 

Show simple item record

Multiple imputation of missing covariates for the Cox proportional hazards cure model
dc.contributor.authorBeesley, Lauren J.
dc.contributor.authorBartlett, Jonathan W.
dc.contributor.authorWolf, Gregory T.
dc.contributor.authorTaylor, Jeremy M. G.
dc.date.accessioned2016-10-17T21:18:02Z
dc.date.available2018-01-08T19:47:52Zen
dc.date.issued2016-11-20
dc.identifier.citationBeesley, Lauren J.; Bartlett, Jonathan W.; Wolf, Gregory T.; Taylor, Jeremy M. G. (2016). "Multiple imputation of missing covariates for the Cox proportional hazards cure model." Statistics in Medicine 35(26): 4701-4717.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/134146
dc.publisherJohn Wiley and Sons, Inc
dc.subject.otherfully conditional specification
dc.subject.othercure models
dc.subject.othermultiple imputation
dc.titleMultiple imputation of missing covariates for the Cox proportional hazards cure model
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelScience
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/134146/1/sim7048_am.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/134146/2/sim7048.pdf
dc.identifier.doi10.1002/sim.7048
dc.identifier.sourceStatistics in Medicine
dc.identifier.citedreferenceRubin DB. Multiple Imputation for Nonresponse in Surveys. John Wiley and Sons, Inc: Hoboken, New Jersey, 1987, 1 – 67.
dc.identifier.citedreferenceMetropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E. Equation of state calculations by fast computing machines. Journal of Chemical Physics 1953; 21: 1087 – 1092.
dc.identifier.citedreferencevan Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pressure covariates in survival analysis. Statistics in Medicine 1999; 18: 681 – 694.
dc.identifier.citedreferenceBreslow NE. Contribution to the discussion of the paper by DR Cox. Journal of the Royal Statistical Society 1972; 24: 216 – 217.
dc.identifier.citedreferenceLittle RJA, Rubin DB. Statistical Analysis with Missing Data 2nd ed. John Wiley and Sons, Inc: Hoboken, New Jersey, 2002. DOI: 10.1002/9781119013563.
dc.identifier.citedreferencevan Buuren S, Groothuis‐Oudshoorn K. Mice: Multivariate imputation by chained equations. Journal of Statistical Software 2011; 45 ( 3 ): 1 – 67.
dc.identifier.citedreferenceHastings WK. Monte carlo sampling methods using markov chains and their applications. Biometrika 1970; 57: 97 – 109.
dc.identifier.citedreferenceLiu J, Gelman A, Hill J, Su YS, Kropko J. On the stationary distribution of iterative imputation. Biometrika 2013; 101 ( 1 ): 155 – 173.
dc.identifier.citedreferenceHughes RA, White IR, Seaman SR, Carpenter JR, Tilling K, Sterne JAC. Joint modeling rationale for chained equations. BMC Medical Research Methodology 2014; 14 ( 28 ): 28 – 38.
dc.identifier.citedreferenceRathouz PJ. Identifiability assumptions for missing covariates in failure time regression models. Biostatistics 2007; 8 ( 2 ): 345 – 356.
dc.identifier.citedreferenceTaylor JMG. Semiparametric estimation in failure time mixture models. Biometrics 1995; 51 ( 3 ): 899 – 907.
dc.identifier.citedreferenceVirani S, Bellile E, Bradford CR, Carey TE, Chepeha DB, Colacino JA, Helman JI, McHugh JB, Peterson LA, Sartor MA, Taylor JMG, Walline HM, Wolf GT, Rozek LS. Ndn and cd1a are novel prognostic methylation markers in patients with nead and neck squamous cell carcinomas. BMC Cancer 2015; 15 ( 825 ): 825 – 838.
dc.identifier.citedreferenceDuffy S, Taylor JMG, Terrell J, Islam M, Yuan Z, Fowler K, Wolf G, Teknos T. Il‐6 predicts recurrence among head and neck cancer patients. Cancer 2008; 113 ( 4 ): 750 – 757.
dc.identifier.citedreferenceTaylor JMG, Murray S, Hsu CH. Survival estimation and testing via multiple imputation. Statistics and Probability Letters 2002; 58: 221 – 232.
dc.identifier.citedreferenceSherlock C, Fearnhead P, Roberts GO. The random walk metropolis: linking theory and practice through a case study. Statistical Science 2010; 25 ( 2 ): 170 – 190.
dc.identifier.citedreferenceFarewell VT. The use of mixture models for the analysis of survival data with long‐term survivors. Biometrics 1982; 38 ( 4 ): 1041 – 1046.
dc.identifier.citedreferenceYamaguchi K. Accelerated failure‐time regression models with a regression model of survival fraction: an application to the analysis of “permanent employment” in Japan. Journal of the American Statistical Association 1992; 87 ( 418 ): 284 – 292.
dc.identifier.citedreferenceLu W, Ying Z. Semiparametric transformation cure models. Biometrika 2004; 91 ( 2 ): 331 – 343.
dc.identifier.citedreferenceKuk AYC, Chen CH. A mixture model combining logistic regression with proportional hazards regression. Biometrika 1992; 79 ( 3 ): 531 – 541.
dc.identifier.citedreferencePeng Y, Dear KBG. A nonparametric mixture model for cure rate estimation. Biometrics 2000; 56 ( 1 ): 237 – 243.
dc.identifier.citedreferenceSy JP, Taylor JMG. Estimation in a Cox proportional hazards cure model. Biometrics 2000; 56: 227 – 236.
dc.identifier.citedreferenceZhuang D, Schenker N, Taylor JMG, Mosseri V, Dubray B. Analysing the effects of anaemia on local recurrence of head and neck cancer when covariate values are missing. Statistics in Medicine 2000; 19: 1237 – 1249.
dc.identifier.citedreferenceCox DR. Regression models and life‐tables. Journal of the Royal Statistical Society 1972; 34 ( 2 ): 187 – 220.
dc.identifier.citedreferenceCho M, Schenker N, Taylor JMG, Zhuang D. Survival analysis with long‐term survivors and partially observed covariates. The Canadian Journal of Statistics 2001; 29 ( 3 ): 421 – 436.
dc.identifier.citedreferenceChen MH, Ibrahim JG. Bayesian methods for missing covariates in cure rate models. Lifetime Data Analysis 2002; 8: 117 – 146.
dc.identifier.citedreferenceMoons KGM, Donders RART, Stijnen T, Harrell FE. Using the outcome for imputation of missing predictor values was preferred. Journal of Clinical Epidemiology 2006; 59: 1092 – 1101.
dc.identifier.citedreferencevan Buuren S, Brands JPL, Groothuis‐Oudshoorn CGM, Rubin DB. Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation 2006; 76 ( 12 ): 1049 – 1064.
dc.identifier.citedreferenceRaghunathan TE. A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology 2001; 27 ( 1 ): 85 – 95.
dc.identifier.citedreferenceBartlett JW, Seaman SR, White IR, Carpenter JR. Multiple imputation of covariates by fully conditional specification: Accomodating the substantive model. Statistical Methods in Medical Research 2014; 24 ( 4 ): 462 – 487.
dc.identifier.citedreferenceWhite IR, Royston P. Imputing missing covariate values for the cox model. Statistics in Medicine 2009; 28: 1982 – 1998.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
sim7048_am.pdf
Size:
433.6KB
Format:
PDF
View/Open
Name:
sim7048.pdf
Size:
240.2KB
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.