A neural network model for survival data

A neural network model for survival data Neural networks have received considerable attention recently, mostly by non‐statisticians. They are considered by many to be very promising tools for classification and prediction. In this paper we present an approach to modelling censored survival data using the input—output relationship associated with a simple feed‐forward neural network as the basis for a non‐linear proportional hazards model. This approach can be extended to other models used with censored survival data. The proportional hazards neural network parameters are estimated using the method of maximum likelihood. These maximum likelihood based models can be compared, using readily available techniques such as the likelihood ratio test and the Akaike criterion. The neural network models are illustrated using data on the survival of men with prostatic carcinoma. A method of interpreting the neural network predictions based on the factorial contrasts is presented. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics in Medicine Wiley

A neural network model for survival data

Statistics in Medicine, Volume 14 (1) – Jan 15, 1995

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Publisher
Wiley
Copyright
Copyright © 1995 John Wiley & Sons, Ltd.
ISSN
0277-6715
eISSN
1097-0258
D.O.I.
10.1002/sim.4780140108
Publisher site
See Article on Publisher Site

Abstract

Neural networks have received considerable attention recently, mostly by non‐statisticians. They are considered by many to be very promising tools for classification and prediction. In this paper we present an approach to modelling censored survival data using the input—output relationship associated with a simple feed‐forward neural network as the basis for a non‐linear proportional hazards model. This approach can be extended to other models used with censored survival data. The proportional hazards neural network parameters are estimated using the method of maximum likelihood. These maximum likelihood based models can be compared, using readily available techniques such as the likelihood ratio test and the Akaike criterion. The neural network models are illustrated using data on the survival of men with prostatic carcinoma. A method of interpreting the neural network predictions based on the factorial contrasts is presented.

Journal

Statistics in MedicineWiley

Published: Jan 15, 1995

References

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