ArticleBMC medical research methodology2017
A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data.
Article in BMC medical research methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
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- Mechanism-centric regulatory network identifies NME2 and MYC programs as markers of Enzalutamide resistance in CRPC.Nature communications · 2024Article
- Survival analysis in breast cancer: evaluating ensemble learning techniques for prediction.PeerJ. Computer science · 2024Article
- Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study.Frontiers in oncology · 2024Article
- Article
- Development and validation of questionnaire-based machine learning models for predicting all-cause mortality in a representative population of China.Frontiers in public health · 2023Article
- Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach.International journal of epidemiology · 2022Article
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4 authors.
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Abstract
backgroundRandom survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data. These methods, however, have been criticised for the bias that results from favouring covariates with many split-points and hence conditional inference forests for time-to-event data have been suggested. Conditional inference forests (CIF) are known to correct the bias in RSF models by separating the procedure for the best covariate to split on from that of the best split point search for the selected covariate.
methodsIn this study, we compare the random survival forest model to the conditional inference model (CIF) using twenty-two simulated time-to-event datasets. We also analysed two real time-to-event datasets. The first dataset is based on the survival of children under-five years of age in Uganda and it consists of categorical covariates with most of them having more than two levels (many split-points). The second dataset is based on the survival of patients with extremely drug resistant tuberculosis (XDR TB) which consists of mainly categorical covariates with two levels (few split-points).
resultsThe study findings indicate that the conditional inference forest model is superior to random survival forest models in analysing time-to-event data that consists of covariates with many split-points based on the values of the bootstrap cross-validated estimates for integrated Brier scores. However, conditional inference forests perform comparably similar to random survival forests models in analysing time-to-event data consisting of covariates with fewer split-points.
conclusionAlthough survival forests are promising methods in analysing time-to-event data, it is important to identify the best forest model for analysis based on the nature of covariates of the dataset in question.
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