%0 Journal Article
%T Nonlinear Survival Regression Using Artificial Neural Network
%A Akbar Biglarian
%A Enayatollah Bakhshi
%A Ahmad Reza Baghestani
%A Mahmood Reza Gohari
%A Mehdi Rahgozar
%A Masoud Karimloo
%J Journal of Probability and Statistics
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/753930
%X 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. 1. Introduction Many different parametric, nonparametric, and semiparametric regression methods are increasingly examined to explore the relationship between a response variable and a set of covariates. The choice of an appropriate method for modeling depends on the methodology of the survey and the nature of the outcome and explanatory variables. A common research question in medical research is to determine whether a set of covariates are correlated with the survival or failure times. Two major characteristics of survival data are censoring and violation of normal assumption for ordinary least squares multiple regressions. These two characteristics of time variable are reasons that straightforward multiple regression techniques cannot be used. Different parametric and semiparametric models in survival regression were introduced which model survival or hazard function. Parametric models, for instance, exponential or weibull, predict survival function while accelerated failure time models are parametric regression methods with logarithm failure time as dependent variable [1, 2]. Choosing an appropriate model for the analysis of the survival data depends on some conditions which are called the underlying assumptions of the model. Sometimes, these assumptions may not be true, for example: (a) lack of independence between consequent waiting times to occurrence of an event or nonproportionality of hazards in semiparametric models, (b) lack of independency of censoring or the distribution of failure times in the case of parametric models [1¨C3]. Although, the Cox regression model is an efficient strategy in analyzing survival data, but when the assumptions of this model are fail, the free assumption methods could be suitable. Artificial neural network (ANN) models, which
%U http://www.hindawi.com/journals/jps/2013/753930/