Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/10075
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dc.contributor.authorBanerjee, Tathagata
dc.contributor.authorMaiti, Tapabrata
dc.contributor.authorMukhopadhyay, Pushpal
dc.date.accessioned2005-10-29T04:01:35Z
dc.date.available2005-10-29T04:01:35Z
dc.date.copyright2005
dc.date.issued2010-10-29T04:01:35Z
dc.identifier.urihttp://hdl.handle.net/11718/10075
dc.descriptionComputational Statistics and Data Analysis, Vol. 51, (2006), pp. 1147 - 55en
dc.description.abstractWe propose a penalized splines (P-splines) based method to predict the pathological stage of localized prostate cancer. A combination of prostate-specific antigen, Gleason histological score, and clinical stage from a cohort study of 834 prostate cancer patients are used to build the P-splines model. It turns out that the proposed methodology results in improved prediction of pathological stage compared to usual logistic regression after removing a few outliers. The improvement is shown to be statistically signifi- cant. Receiver-operating characteristic (ROC) curve is drawn and we show that the increase in area under the ROC curve over the commonly used logistic regression based classification method is also statistically significant. © 2005 Elsevier B.V. All rights reserved.
dc.language.isoenen
dc.subjectLogistic Regressionen
dc.subjectNomogramen
dc.subjectOrgan-Confined Diseaseen
dc.subjectReceiver-Operating Characteristic Curveen
dc.titleClassification of pathological stages of prostate cancer using penalized splinesen
dc.typeArticleen
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