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. Author manuscript; available in PMC: 2023 Aug 1.
Published in final edited form as: Kidney Int. 2022 May 23;102(2):370–381. doi: 10.1016/j.kint.2022.04.022

Figure 4. Paths of regression coefficient for proteins and clinical factors selected as predictors of risk of 10-year ESKD shrinking towards zero using penalized LASSO logistic regression.

Figure 4.

A total of 11 candidate proteins and clinical factors are included in the least absolute shrinkage and selection operator (LASSO) regression model and the coefficient of 8 selected variables are shown. Optimal lambda, a penalty factor for penalized maximum likelihood estimation, was calculated by 10-fold cross-validation at its minimum level. Each curve corresponds to a protein selected as a result of shrinkage for selection, and draws shrinkage during estimation of regression coefficient. LASSO penalizes the sum of the absolute values of regression coefficients, and a predictor with a coefficient of zero was excluded from the model and was not presented in the figure.

C-statistics of logistic regression for clinical model (HbA1c, log2ACR, and eGFR/10): 0.847. C-statistics of logistic regression for selected variables by LASSO regression analysis (HbA1c, log2ACR, eGFR/10, LAYN. ESAM. DLL1, MAPK11, endostatin): 0.869.

Clinical model vs New model: Difference in C-statistics, 0.022 (P=0.006); NRI, 0.55 (P<0.0001).