Comparison of prediction ability of two extended Cox models in nonlinear survival data analysis.
10.12122/j.issn.1673-4254.2023.01.10
- Author:
Yu Xuan CHEN
1
;
Hong Xia WEI
1
;
Jian Hong PAN
2
;
Sheng Li AN
1
Author Information
1. Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, China.
2. Center for Drug Evaluation, National Medical Products Administration, Beijing 100022, China.
- Publication Type:Journal Article
- Keywords:
Cox model;
deep neural network;
nonlinear correlation;
restricted cubic spline;
survival analysis
- MeSH:
Proportional Hazards Models;
Survival Analysis;
Calibration;
Computer Simulation;
Data Analysis
- From:
Journal of Southern Medical University
2023;43(1):76-84
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To compare the predictive ability of two extended Cox models in nonlinear survival data analysis.
METHODS:Through Monte Carlo simulation and empirical study and with the conventional Cox Proportional Hazards model and Random Survival Forests as the reference models, we compared restricted cubic spline Cox model (Cox_RCS) and DeepSurv neural network Cox model (Cox_DNN) for their prediction ability in nonlinear survival data analysis. Concordance index was used to evaluate the differentiation of the prediction results (a larger concordance index indicates a better prediction ability of the model). Integrated Brier Score was used to evaluate the calibration degree of the prediction (a smaller index indicates a better prediction ability).
RESULTS:For data that met requirement of the proportion risk, the Cox_RCS model had the best prediction ability regardless of the sample size or deletion rate. For data that failed to meet the proportion risk, the prediction ability of Cox_DNN was optimal for a large sample size (≥500) with a low deletion (< 40%); the prediction ability of Cox_RCS was superior to those of other models in all other scenarios. For example data, the Cox_RCS model showed the best performance.
CONCLUSION:In analysis of nonlinear low maintenance data, Cox_RCS and Cox_DNN have their respective advantages and disadvantages in prediction. The conventional survival analysis methods are not inferior to machine learning or deep learning methods under certain conditions.
