A NEURAL-NETWORK MODEL FOR SURVIVAL-DATA
A NEURAL-NETWORK MODEL FOR SURVIVAL-DATA
复制标题
DOI：
10.1002/sim.4780140108
复制
发表时间：
1995-01-15
影响因子：
2
通讯作者：
SIMON, R
中科院分区：
文献类型：
--
作者：
FARAGGI, D;SIMON, R
神经网络最近受到了相当大的关注，主要是非统计学家。它们被许多人认为是非常有前途的分类和预测工具。在本文中，我们提出了一种方法来建模删失生存数据使用的输入输出关系与一个简单的前馈神经网络作为基础的非线性比例风险模型。这种方法可以扩展到其他模型与删失生存数据。采用最大似然法对比例风险神经网络参数进行了估计。这些基于最大似然的模型可以比较，使用现成的技术，如似然比检验和赤池准则。神经网络模型说明使用数据的男性前列腺癌的生存。提出了一种基于因子对比的神经网络预测解释方法。
Neural networks have received considerable attention recently, mostly by non-statisticians. They are considered by many to be very promising tools for classification and prediction. In this paper we present an approach to modelling censored survival data using the input-output relationship associated with a simple feed-forward neural network as the basis for a non-linear proportional hazards model. This approach can be extended to other models used with censored survival data. The proportional hazards neural network parameters are estimated using the method of maximum likelihood. These maximum likelihood based models can be compared, using readily available techniques such as the likelihood ratio test and the Akaike criterion. The neural network models are illustrated using data on the survival of men with prostatic carcinoma. A method of interpreting the neural network predictions based on the factorial contrasts is presented.