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Nonlinear Survival Regression Using Artificial Neural Network
Joint Authors
Gohari, Mahmood Reza
Baghestani, Ahmad Rida
Karimlou, Masoud
Rahgozar, Mehdi
Bakhshi, Enayatollah
Biglarian, Akbar
Source
Journal of Probability and Statistics
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-02-21
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Biglarian, Akbar& Bakhshi, Enayatollah& Baghestani, Ahmad Rida& Gohari, Mahmood Reza& Rahgozar, Mehdi& Karimlou, Masoud. 2013. Nonlinear Survival Regression Using Artificial Neural Network. Journal of Probability and Statistics،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-496097
Modern Language Association (MLA)
Biglarian, Akbar…[et al.]. Nonlinear Survival Regression Using Artificial Neural Network. Journal of Probability and Statistics No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-496097
American Medical Association (AMA)
Biglarian, Akbar& Bakhshi, Enayatollah& Baghestani, Ahmad Rida& Gohari, Mahmood Reza& Rahgozar, Mehdi& Karimlou, Masoud. Nonlinear Survival Regression Using Artificial Neural Network. Journal of Probability and Statistics. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-496097
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-496097