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Nonlinear Survival Regression Using Artificial Neural Network

Author

Listed:
  • Akbar Biglarian
  • Enayatollah Bakhshi
  • Ahmad Reza Baghestani
  • Mahmood Reza Gohari
  • Mehdi Rahgozar
  • Masoud Karimloo

Abstract

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.

Suggested Citation

  • Akbar Biglarian & Enayatollah Bakhshi & Ahmad Reza Baghestani & Mahmood Reza Gohari & Mehdi Rahgozar & Masoud Karimloo, 2013. "Nonlinear Survival Regression Using Artificial Neural Network," Journal of Probability and Statistics, Hindawi, vol. 2013, pages 1-7, February.
    • Handle: RePEc:hin:jnljps:753930
    DOI: 10.1155/2013/753930
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