{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101718"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101718","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"DWCox: A density-weighted Cox model for outlier-robust prediction of prostate cancer survival","abstract":"Reliable predictions on the risk and survival time of prostate cancer patients based on their clinical records can help guide their treatment and provide hints about the disease mechanism. The Cox regression is currently a commonly accepted approach for such tasks in clinical applications. More complex methods, like ensemble approaches, have the potential of reaching better prediction accuracy at the cost of increased training difficulty and worse result interpretability. Better performance on a specific data set may also be obtained by extensive manual exploration in the data space, but such developed models are subject to overfitting and usually not directly applicable to a different data set. We propose \\model, a density-weighted Cox model that has improved robustness against outliers and thus can provide more accurate predictions of prostate cancer survival. \\Model~assigns weights to the training data according to their local kernel density in the feature space, and incorporates those weights into the partial likelihood function. A linear regression is then used to predict the actual survival times from the predicted risks. In \\challengefull, \\model~obtained the best average ranking in prediction accuracy on the risk and survival time. The success of \\model~is remarkable given that it is one of the smallest and most interpretable models submitted to the challenge. In simulations, \\model~performed consistently better than a standard Cox model when the training data contained many sparsely distributed outliers. Although developed for prostate cancer patients, \\model~can be easily re-trained and applied to other survival analysis problems. \\Model~is implemented in R and can be downloaded from https://github.com/JinfengXiao/DWCox.","abstract_html":"Reliable predictions on the risk and survival time of prostate cancer patients based on their clinical records can help guide their treatment and provide hints about the disease mechanism. The Cox regression is currently a commonly accepted approach for such tasks in clinical applications. More complex methods, like ensemble approaches, have the potential of reaching better prediction accuracy at the cost of increased training difficulty and worse result interpretability. Better performance on a specific data set may also be obtained by extensive manual exploration in the data space, but such developed models are subject to overfitting and usually not directly applicable to a different data set. We propose \\model, a density-weighted Cox model that has improved robustness against outliers and thus can provide more accurate predictions of prostate cancer survival. \\Model~assigns weights to the training data according to their local kernel density in the feature space, and incorporates those weights into the partial likelihood function. A linear regression is then used to predict the actual survival times from the predicted risks. In \\challengefull, \\model~obtained the best average ranking in prediction accuracy on the risk and survival time. The success of \\model~is remarkable given that it is one of the smallest and most interpretable models submitted to the challenge. In simulations, \\model~performed consistently better than a standard Cox model when the training data contained many sparsely distributed outliers. Although developed for prostate cancer patients, \\model~can be easily re-trained and applied to other survival analysis problems. \\Model~is implemented in R and can be downloaded from https://github.com/JinfengXiao/DWCox.","abstract_has_math":false,"creators":["Xiao, Jinfeng"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-27T16:34:21Z","date_published":"2018-09-27T16:34:21Z","updated_at":"2026-07-22T22:24:40Z","subjects":["DREAM, Prostate cancer, Cox model"],"languages":["en"],"rights":["Copyright 2018 Jinfeng Xiao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101718","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei"]},{"key":"dc:creator","label":"Author","values":["Xiao, Jinfeng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-27T16:34:21Z","2020-09-28T09:15:13Z","2018-07-16","2018-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["DREAM, Prostate cancer, Cox model"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Jinfeng Xiao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101718"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Reliable predictions on the risk and survival time of prostate cancer patients based on their clinical records can help guide their treatment and provide hints about the disease mechanism. The Cox regression is currently a commonly accepted approach for such tasks in clinical applications. More complex methods, like ensemble approaches, have the potential of reaching better prediction accuracy at the cost of increased training difficulty and worse result interpretability. Better performance on a specific data set may also be obtained by extensive manual exploration in the data space, but such developed models are subject to overfitting and usually not directly applicable to a different data set. We propose \\model, a density-weighted Cox model that has improved robustness against outliers and thus can provide more accurate predictions of prostate cancer survival. \\Model~assigns weights to the training data according to their local kernel density in the feature space, and incorporates those weights into the partial likelihood function. A linear regression is then used to predict the actual survival times from the predicted risks. In \\challengefull, \\model~obtained the best average ranking in prediction accuracy on the risk and survival time. The success of \\model~is remarkable given that it is one of the smallest and most interpretable models submitted to the challenge. In simulations, \\model~performed consistently better than a standard Cox model when the training data contained many sparsely distributed outliers. Although developed for prostate cancer patients, \\model~can be easily re-trained and applied to other survival analysis problems. \\Model~is implemented in R and can be downloaded from https://github.com/JinfengXiao/DWCox.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-08-01","The student, Jinfeng Xiao, accepted the attached license on 2018-07-13 at 16:11.","The student, Jinfeng Xiao, submitted this Thesis for approval on 2018-07-13 at 16:18.","This Thesis was approved for publication on 2018-07-16 at 08:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12874 on 2018-09-27 at 11:19:21","Made available in DSpace on 2018-09-27T16:34:21Z (GMT). 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The Cox regression is currently a commonly accepted approach for such tasks in clinical applications. More complex methods, like ensemble approaches, have the potential of reaching better prediction accuracy at the cost of increased training difficulty and worse result interpretability. Better performance on a specific data set may also be obtained by extensive manual exploration in the data space, but such developed models are subject to overfitting and usually not directly applicable to a different data set. We propose \\model, a density-weighted Cox model that has improved robustness against outliers and thus can provide more accurate predictions of prostate cancer survival. \\Model~assigns weights to the training data according to their local kernel density in the feature space, and incorporates those weights into the partial likelihood function. A linear regression is then used to predict the actual survival times from the predicted risks. In \\challengefull, \\model~obtained the best average ranking in prediction accuracy on the risk and survival time. The success of \\model~is remarkable given that it is one of the smallest and most interpretable models submitted to the challenge. In simulations, \\model~performed consistently better than a standard Cox model when the training data contained many sparsely distributed outliers. Although developed for prostate cancer patients, \\model~can be easily re-trained and applied to other survival analysis problems. \\Model~is implemented in R and can be downloaded from https://github.com/JinfengXiao/DWCox.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-08-01","The student, Jinfeng Xiao, accepted the attached license on 2018-07-13 at 16:11.","The student, Jinfeng Xiao, submitted this Thesis for approval on 2018-07-13 at 16:18.","This Thesis was approved for publication on 2018-07-16 at 08:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12874 on 2018-09-27 at 11:19:21","Made available in DSpace on 2018-09-27T16:34:21Z (GMT). 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