Nonlinear Survival Regression Using Artificial Neural Network

被引：8

作者：

Biglarian, Akbar ^{[1
]}

Bakhshi, Enayatollah ^{[1
]}

Baghestani, Ahmad Reza ^{[2
]}

Gohari, Mahmood Reza ^{[3
]}

Rahgozar, Mehdi ^{[1
]}

Karimloo, Masoud ^{[1
]}

机构：

[1] USWRS, Dept Biostat, Tehran 1985713834, Iran

[2] Shahid Beheshti Univ Med Sci, Fac Paramed Sci, Dept Biostat, Tehran 1971653313, Iran

[3] Tehran Univ Med Sci, Hosp Management Res Ctr, Tehran 1996713883, Iran

来源：

JOURNAL OF PROBABILITY AND STATISTICS | 2013年 / 2013卷

关键词：

D O I：

10.1155/2013/753930

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页数：7

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