Title: Comparison between BP neural network and Cox proportional hazard model in survival analysis
Abstract: Aim: To compare their prediction performance of BP neural network model and Cox proportion hazard model in survival analysis and to explore the superiority of BP neural network model in survival analysis. Methods: Monte Carlo was used to generate the data sets under the condition of different sample size,different degree of censoring,number of variable and interactions,non-linear effect,distinct distribution of covariate and proportional vs non-proportional hazard.Then BP neural network model and Cox model were built,and their prediction performance was compared using concord-ance index C. Results: In the research on simulation data sets,when the sample size of 100,proportion of censoring of60%,80%,and sample size of 300,proportion of censoring of 80%,BP neural network model performed superior to Cox model( P 0. 05). And when the covariates don' t meet PH assumption and had three-way interaction,non-linear effect,BP neural network performed superior to Cox model( P 0. 05). In the real data,BP neural network model's concordance index was 0. 835,which performed superior to Cox model( tpaired= 4. 311,P 0. 001). Conclusion: For the small sample size,high and the covariates don't meet PH assumption and has three-way interaction,non-linear effect data sets,BP neural network has better advantage than Cox model. It is worth to popularize further in survival analysis.
Publication Year: 2014
Publication Date: 2014-01-01
Language: en
Type: article
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