Application of conditional inference forest in time-to-event data analysis.
10.12122/j.issn.1673-4254.2020.04.05
- Author:
Yingxin LIU
1
;
Pei KANG
1
;
Jun XU
2
;
Shengli AN
1
Author Information
1. Department of Biostatistics, School of Public Health, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
2. Department of Economic Management, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
- Publication Type:Journal Article
- Keywords:
accelerated failure time models;
conditional inference forests;
proportional hazards models;
random survival forests;
survival analysis
- MeSH:
Data Analysis;
Proportional Hazards Models;
Sample Size;
Survival Analysis
- From:
Journal of Southern Medical University
2020;40(4):475-482
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To explore the application and advantages of conditional inference forest in survival analysis.
METHODS:We used simulated experiment and actual data to compare the predictive performance of 4 models, including Coxproportional hazards model, accelerated failure time model, random survival forest model and conditional inference forest model based on their Brier scores.
RESULTS:Simulation experiment suggested that both of the two forest models had more accurate and robust predictive performance than the other two regression models. Conditional inference forest model was superior to the other models in analyzing time-to-event data with polytomous covariates, collinearity or interaction, especially for a large sample size and a high censoring rate. The results of actual data analysis demonstrated that conditional inference forest model had the best predictive performance among the 4 models.
CONCLUSIONS:Compared with the commonly used survival analysis methods, conditional inference forest model performs better especially when the data contain polytomous covariates with collinearity and interaction.
