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. 2021 Jan 7;11:587378. doi: 10.3389/fgene.2020.587378

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Copyright © 2021 Liu and Li.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Performance of the proposed random forest estimator compared with other methods for L = 3, 4, 5. The left penal is the MAE across of 10-fold cross-validation. The right panel is the RMSE across of 10-fold cross-validation. SRF, proposed random forest estimator; Naive.km, estimate based on Kaplan–Meier estimator without adjusting for the covariates; Naive.Cox, Cox regression based estimator; Lu.id, method of Tian et al. (2014) with identity link; Lu.exp, method of Tian et al. (2014) with exponential link; Wang.id method of Wang and Schaubel (2018) with identity link; Wang:exp, method of Wang and Schaubel (2018) with exponential link.