Journal of Probability and Statistics
Volume 2013 (2013), Article ID 753930, 7 pages
Research Article

Nonlinear Survival Regression Using Artificial Neural Network

1Department of Biostatistics, University of Social Welfare and Rehabilitation Sciences (USWRS), Tehran 1985713834, Iran
2Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran 1971653313, Iran
3Hospital Management Research Center, Tehran University of Medical Sciences (TUMS), Tehran 1996713883, Iran

Received 9 May 2012; Revised 21 November 2012; Accepted 23 November 2012

Academic Editor: Shein-chung Chow

Copyright © 2013 Akbar Biglarian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Survival analysis methods deal with a type of data, which is waiting time till occurrence of an event. One common method to analyze this sort of data is Cox regression. Sometimes, the underlying assumptions of the model are not true, such as nonproportionality for the Cox model. In model building, choosing an appropriate model depends on complexity and the characteristics of the data that effect the appropriateness of the model. One strategy, which is used nowadays frequently, is artificial neural network (ANN) model which needs a minimal assumption. This study aimed to compare predictions of the ANN and Cox models by simulated data sets, which the average censoring rate were considered 20% to 80% in both simple and complex model. All simulations and comparisons were performed by R 2.14.1.