ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2020
[Application of conditional inference forest in time-to-event data analysis].
Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Health-related quality of life among healthcare workers: a comparative analysis using regression, conditional tree and forests.BMC public health · 2025Article
- [Parameter estimation using time-dependent Weibull proportional hazards model for survival analysis with partly interval censored data].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis.BMC public health · 2023Article
- [Comparison of prediction ability of two extended Cox models in nonlinear survival data analysis].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo explore the application and advantages of conditional inference forest in survival analysis.
methodsWe 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.
resultsSimulation 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.
conclusionsCompared with the commonly used survival analysis methods, conditional inference forest model performs better especially when the data contain polytomous covariates with collinearity and interaction.
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