Journal
BIOMETRICAL JOURNAL
Volume 65, Issue 5, Pages -Publisher
WILEY
DOI: 10.1002/bimj.202200153
Keywords
Buckley-James model; ensemble learning; extreme learning machine; random survival forests; survival analysis
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In this paper, a novel regression model, named ELM-based BJ boosting model, is proposed to overcome the limitation of traditional BJ model in processing nonlinear problems. The model applies RSF for covariates imputation, develops an ensemble of ELMs for regression, and replaces the linear combination of covariates in BJ model. The ELM-based BJ boosting model outperforms traditional BJ model, two types of BJ boosting models, RSF, and Cox proportional hazards model in both simulation studies and real data applications in terms of concordance index and integrated Brier score.
Buckley-James (BJ) model is a typical semiparametric accelerated failure time model, which is closely related to the ordinary least squares method and easy to be constructed. However, traditional BJ model built on linearity assumption only captures simple linear relationships, while it has difficulty in processing nonlinear problems. To overcome this difficulty, in this paper, we develop a novel regression model for right-censored survival data within the learning framework of BJ model, basing on random survival forests (RSF), extreme learning machine (ELM), and L-2 boosting algorithm. The proposed method, referred to as ELM-based BJ boosting model, employs RSF for covariates imputation first, then develops a new ensemble of ELMs-ELM-based boosting algorithm for regression by ensemble scheme of L-2 boosting, and finally, uses the output function of the proposed ELM-based boosting model to replace the linear combination of covariates in BJ model. Due to fitting the logarithm of survival time with covariates by the nonparametric ELM-based boosting method instead of the least square method, the ELM-based BJ boosting model can capture both linear covariate effects and nonlinear covariate effects. In both simulation studies and real data applications, in terms of concordance index and integrated Brier sore, the proposed ELM-based BJ boosting model can outperform traditional BJ model, two kinds of BJ boosting models proposed by Wang et al., RSF, and Cox proportional hazards model.
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