Analysis of Survival Data Having Time-Dependent Covariates

被引：8
|
作者：
Tsujitani, Masaaki [1 ]
Sakon, Masato [2 ]
机构：
[1] Osaka Electrocommun Univ, Dept Informat Engn, Osaka 6580032, Japan
[2] Nishinomiya Municipal Cent Hosp, Nishinomiya, Hyogo 6638014, Japan
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2009年 / 20卷 / 03期
关键词：
Bootstrapping; Cox's proportional hazards model; neural network model; partial logistic regression models; time-dependent covariates; PRIMARY BILIARY-CIRRHOSIS; LOGISTIC-REGRESSION; UPDATING PROGNOSIS; MODEL SELECTION; INFORMATION;
D O I：
10.1109/TNN.2008.2008328
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Cox's proportional hazards model has been widely used for the analysis of treatment and prognostic effects with censored survival data. In this paper, we propose a neural network model based on bootstrapping to estimate the survival function and predict the short-term survival at any time during the course of the disease. The bootstrapping for the neural network is introduced when selecting the optimum number of hidden units and testing the goodness-of-fit. The proposed methods are illustrated using data from a long-term study of patients with primary biliary cirrhosis (PBC).
引用
收藏
页码：389 / 394
页数：6
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