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Updated: Aug 13, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Comparison of Cox regression with other methods for determining prediction models and nomograms
1Department of Epidemiology and Biostatistics, Memorial-Sloan Kettering Cancer Center, New York, NY 10021, USA. kattanm@mskcc.org
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.
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