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. 2019 Dec 31;20:1. doi: 10.1186/s12874-019-0863-0

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© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 2 — Comparison of Discrimination for Sudden Cardiac Arrest (SCA) Prediction with Different Random Forests Approaches. a, b, c Time-varying AUC curves for the RSF approach which uses only baseline covariates (panel a), RF-SLAM approach with only baseline covariates (panel b), RF-SLAM approach with both baseline and time-varying covariates (panel c). d, e, f Predicted survival curves from RSF (panel d), RF-SLAM approach with only baseline covariates (panel e), and RF-SLAM approach with both baseline and time-varying covariates (panel f). Individuals who experienced an SCA are colored-coded in red and all others are colored-coded in green. Note each column of plots corresponds to the same model (i.e. the left column corresponds to the RSF approach, center column corresponds to the RF-SLAM approach with only baseline covariate, and the right column corresponds to the RF-SLAM approach with both baseline and time-varying covariates)