Comparison of Cox regression with other methods for determining prediction models and nomograms Journal Article
| Author: | Kattan, M. W. |
| Article Title: | Comparison of Cox regression with other methods for determining prediction models and nomograms |
| Abstract: | Purpose: There is controversy as to whether artificial neural networks and other machine learning methods provide predictions that are more accurate than those provided by traditional statistical models when applied to censored data. Materials and Methods: Several machine learning prediction methods are compared with Cox proportional hazards regression using 3 large urological datasets. As a measure of predictive ability, discrimination that is similar to an area under the receiver operating characteristic curve is computed for each. Results: In all 3 datasets Cox regression provided comparable or superior predictions compared with neural networks and other machine learning techniques. In general, this finding is consistent with the literature. Conclusions: Although theoretically attractive, artificial neural networks and other machine learning techniques do not often provide an improvement in predictive accuracy over Cox regression. |
| Keywords: | cancer survival; survival analysis; major clinical study; cancer recurrence; conference paper; proportional hazards models; kidney carcinoma; kidney neoplasms; prostate cancer; prostatic neoplasms; cancer center; carcinoma, renal cell; models, statistical; predictive value of tests; brachytherapy; radiotherapy, conformal; statistical model; nomogram; receiver operating characteristic; artificial neural network; neural networks (computer); machine learning; nonbiological model; humans; prognosis; human; male; priority journal; neural network models |
| Journal Title: | Journal of Urology |
| Volume: | 170 |
| Issue: | 6 Suppl. |
| ISSN: | 0022-5347 |
| Publisher: | Elsevier Science, Inc. |
| Date Published: | 2003-12-01 |
| Start Page: | S6 |
| End Page: | S10 |
| Language: | English |
| DOI: | 10.1097/01.ju.0000094764.56269.2d |
| PUBMED: | 14610404 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Export Date: 25 September 2014 -- Source: Scopus |
