Comparison of Cox regression with other methods for determining prediction models and nomograms.
OBJECTIVE
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.


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.
Kattan
Michael W
MW
Department of Epidemiology and Biostatistics, Memorial-Sloan Kettering Cancer Center, New York, NY 10021, USA. kattanm@mskcc.org
D001918:Brachytherapy; D002292:Carcinoma, Renal Cell / Q000401:mortality*; D006801:Humans; D007680:Kidney Neoplasms / Q000401:mortality*; D008297:Male; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D011471:Prostatic Neoplasms / Q000401:mortality*; D020266:Radiotherapy, Conformal; D016019:Survival Analysis