Nonlinear Survival Regression Using Artificial Neural Network

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, c...

Full description

Saved in:
Bibliographic Details
Published in:Journal of probability and statistics 2013-01, Vol.2013 (2013), p.1-7
Main Authors: Biglarian, Akbar, Bakhshi, Enayatollah, Baghestani, Ahmad Rida, Gohari, Mahmood Reza, Rahgozar, Mehdi, Karimlou, Masoud
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer simulation
Learning theory
Mathematical models
Neural networks
Regression
Statistics
Survival
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1687-952X
1687-9538
DOI:10.1155/2013/753930