Table 8. Comparison of the discrimination ability of the predictive models with DeLong-Test (N = 121). Types of correction mechanisms compared in motor performance models AUC [95% CI]corrected scores* ∆ = AUC [95% CI]corrected scores - AUC [95% CI]raw scores†  z p Mirwald .73 [.63; .82] .00 [–.05; .04] –0.10 .922 Moore-1 .73 [.64; .82] .00 [–.04; .05] 0.06 .950 Moore-2 .73 [.64; .82] .00 [–.03; .03] 0.15 .879 Fransen-1 .73 [.64; .82] .00 [–.05; .05] –0.02 .981 Fransen-2 .73 [.64; .82] .00 [–.05; .05] 0.04 .972 %PAH .71 [.61; .80] -.02 [–.07; .02] –0.92 .356 %RAH .76 [.67; .84] .03 [–.01; .07] 1.33 .182 %PAH = attained percentage of the predicted adult height, %RAH = attained percentage of the real adult height; AUC = area under the curve. Adjusted %-level (Bonferroni correction) = 0.007. *AUC [95% CI]corrected scores = These statistics represent the AUC based on the regression model using corrected scores. † AUC [95% CI]raw scores = This statistic represents the AUC based on the binary regression model using raw scores and corresponds to 0.73 [0.64; 0.82].