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. Author manuscript; available in PMC: 2021 Nov 1.
Published in final edited form as: Transpl Int. 2020 Jul 28;33(11):1472–1480. doi: 10.1111/tri.13695

Figure 1. Predictive performance of regression (Reg), gradient boosting (GB), and random forests (RF) in predicting kidney transplant outcomes, as measured in the C-statistic.

Figure 1.

DGF, delayed graft function; AR, one-year acute rejection; DCGF, death-censored graft failure; and ACGF, all-cause graft failure. Regression represents logistic regressions for delayed graft function and acute rejection, and Cox regressions for death-censored graft failure, death, and all-cause graft failure. Y-axis indicates the area under the receiver operating characteristic curve (AUROC) for delayed graft function and acute rejection, and Harrell’s concordance statistic for death-censored graft failure, death, and all-cause graft failure.