View Item 

Show simple item record

Real‐Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models
dc.contributor.authorTaylor, Jeremy M. G.en_US
dc.contributor.authorPark, Yongseoken_US
dc.contributor.authorAnkerst, Donna P.en_US
dc.contributor.authorProust‐lima, Cecileen_US
dc.contributor.authorWilliams, Scotten_US
dc.contributor.authorKestin, Larryen_US
dc.contributor.authorBae, Kyoungwhaen_US
dc.contributor.authorPickles, Tomen_US
dc.contributor.authorSandler, Howarden_US
dc.date.accessioned2013-05-02T19:35:23Z
dc.date.available2014-05-01T14:28:33Zen_US
dc.date.issued2013-03en_US
dc.identifier.citationTaylor, Jeremy M. G.; Park, Yongseok; Ankerst, Donna P.; Proust‐lima, Cecile ; Williams, Scott; Kestin, Larry; Bae, Kyoungwha; Pickles, Tom; Sandler, Howard (2013). "Realâ Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models." Biometrics 69(1): 206-213. <http://hdl.handle.net/2027.42/97517>en_US
dc.identifier.issn0006-341Xen_US
dc.identifier.issn1541-0420en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/97517
dc.publisherWiley Periodicals, Inc.en_US
dc.publisherCRC Tayloren_US
dc.subject.otherProstate Canceren_US
dc.subject.otherOnline Calculatoren_US
dc.subject.otherJoint Longitudinal‐Survival Modelen_US
dc.subject.otherPSAen_US
dc.subject.otherPredicted Probabilityen_US
dc.titleReal‐Time Individual Predictions of Prostate Cancer Recurrence Using Joint Modelsen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.identifier.pmid23379600en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/97517/1/biom1823-sup-0001-Data1.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/97517/2/biom1823.pdf
dc.identifier.doi10.1111/j.1541-0420.2012.01823.xen_US
dc.identifier.sourceBiometricsen_US
dc.identifier.citedreferenceKennedy, E. H., Taylor, J. M. G., Schaubel, D. E., and Williams, S. ( 2010 ). The effect of salvage therapy on survival in a longitudinal study with treatment by indication. Statistics in Medicine 29, 2569 – 2580.en_US
dc.identifier.citedreferenceHenderson, R., Diggle, P., and Dobson, A. ( 2000 ). Joint modelling of longitudinal measurements and event time data. Biostatistics 1, 465 – 480.en_US
dc.identifier.citedreferenceProust‐Lima, C., Taylor, J. M. G., Williams, S. G., Ankerst, D. P., Liu, N., Kestin, L. L., Bae, K., and Sandler, H. M. ( 2008 ). Determinants of change in prostate‐specific antigen over time and its association with recurrence after external beam radiation therapy for prostate cancer in five large cohorts. International Journal of Radiation Oncology, Biology, Physics 72, 782 – 791.en_US
dc.identifier.citedreferenceProust‐Lima, C. and Taylor, J. M. G. ( 2009 ). Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: A joint modeling approach. Biostatistics 10, 535 – 549.en_US
dc.identifier.citedreferenceProust‐Lima, C., Sène, M., Taylor, J. M. G., and Jacqmin‐Gadda, H. ( 2012 ). Joint latent class models for longitudinal and time‐to‐event data: A review. Statistical Methods in Medical Research to appear.en_US
dc.identifier.citedreferencePickles, T., Kim‐Sing, C., Morris, W., Tyldesley, S., and Paltiel, C. ( 2003 ). Evaluation of the Houston biochemical relapse definition in men treated with prolonged neoadjuvant and adjuvant androgen ablation and assessment of follow‐up lead‐time bias. International Journal of Radiation Oncology, Biology, Physics 57, 11 – 18.en_US
dc.identifier.citedreferenceLin, H., Turnbull, B. W., McCulloch, C. E., and Slate, E. H. ( 2002 ). Latent class models for joint analysis of longitudinal biomarker and event process data. Journal of the American Statistical Association 97, 53 – 65.en_US
dc.identifier.citedreferenceAnkerst, D. P. and Finkelstein, D. M. ( 2006 ). Clinical monitoring based on joint models for longitudinal biomarkers and event times. Handbook of Statistics in Clinical Oncology, 2nd ed., JC Crowley and DP Ankerst (eds), 397 – 418. Boca Raton, CRC Taylor.en_US
dc.identifier.citedreferenceYu, M., Taylor, J. M. G., and Sandler, H. M. ( 2008 ). Individual prediction in prostate cancer studies using a joint longitudinal survival‐cure model. Journal of the American Statistical Association 103, 178 – 187.en_US
dc.identifier.citedreferenceYu, M., Law, N. J., Taylor, J. M. G., and Sandler, H. M. ( 2004 ). Joint longitudinal‐survival‐cure models and their application to prostate cancer. Statistica Sinica 14, 835 – 862.en_US
dc.identifier.citedreferenceXu, J. and Zeger, S. L. ( 2001 ). Joint analysis of longitudinal data comprising repeated measures and times to events. Journal of the Royal Statistical Society. Series C (Applied Statistics) 50, 375 – 387.en_US
dc.identifier.citedreferenceWang, Y. and Taylor, J. M. G. ( 2001 ). Jointly modeling longitudinal and event time data with application to acquired immunodeficiency syndrome. Journal of the American Statistical Association 96, 895 – 905.en_US
dc.identifier.citedreferenceTaylor, J. M. G., Yu, M., and Sandler, H. M. ( 2005 ). Individualized predictions of disease progression following radiation therapy for prostate cancer. Journal of Clinical Oncology 23, 816 – 825.en_US
dc.identifier.citedreferenceTaylor, J. M. G., Ankerst, D. P., and Andridge, R. R. ( 2008 ). Validation of biomarker‐based risk prediction models. Clinical Cancer Research 14, 5977 – 5983.en_US
dc.identifier.citedreferenceSteyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J., and Kattan, M. W. ( 2010 ). Assessing the performance of prediction models: A framework for traditional and novel measures. Epidemiology 21, 128 – 138.en_US
dc.identifier.citedreferenceRoach, 3rd, M., Hanks, G., Thames, Jr., H., Schellhammer, P., Shipley, W. U., Sokol, G. H., and Sandler, H. ( 2006 ). Defining biochemical failure following radiotherapy with or without hormonal therapy in men with clinically localized prostate cancer: Recommendations of the RTOG‐ASTRO Phoenix Consensus Conference. International Journal of Radiation Oncology, Biology, Physics 65, 965 – 974.en_US
dc.identifier.citedreferenceRizopoulos, D. ( 2011 ). Dynamic predictions and prospective accuracy in joint models for longitudinal and time‐to‐event data. Biometrics 67, 819 – 829.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
biom1823-sup-0001-Data1.pdf
Size:
293.6KB
Format:
PDF
View/Open
Name:
biom1823.pdf
Size:
433.0KB
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.