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Table 2 Effect of removing and adding variables from the prognostic model (Probit model with 3df) on discrimination (D) and explained variation ( \( {\boldsymbol{R}}_{\boldsymbol{D}}^{\mathbf{2}} \) )

From: Prognostic survival model for people diagnosed with invasive cutaneous melanoma

  Removing variable Adding variable
Variable Singlea Cumulativeb Single variable only Added to thickness
  D \( {\boldsymbol{R}}_{\boldsymbol{D}}^{\mathbf{2}} \) Order D \( {\boldsymbol{R}}_{\boldsymbol{D}}^{\mathbf{2}} \) D \( {\boldsymbol{R}}_{\boldsymbol{D}}^{\mathbf{2}} \) Harrell’s C Dd \( {\boldsymbol{R}}_{\boldsymbol{D}}^{\mathbf{2}} \) d
<full model> 1.526 0.478         
Clark’s level 1.521 0.476 1 1.521 0.476 1.108 0.325 0.815 0.006 0.002
Morphology 1.519 0.475 2 1.513 0.473 0.643 0.140 0.699 0.017 0.006
Gender 1.513 0.473 3 1.500 0.469 0.277 0.029 0.569 0.038 0.014
Agec 1.504 0.470 4 1.467 0.458 0.398 0.058 0.646 0.042 0.015
Positive lymph nodes 1.504 0.470 5 1.457 0.451 0.990 0.278 0.541 0.038 0.014
Ulceration 1.503 0.470 6 1.409 0.438 0.998 0.281 0.715 0.044 0.016
metastasis 1.494 0.467 7 1.358 0.420 1.068 0.309 0.530 0.053 0.019
Body site 1.490 0.466 8 1.307 0.401 0.303 0.035 0.600 0.052 0.019
Thicknessc 1.354 0.419 9 - - 1.307 0.401 0.866   
  1. aBased on the prognostic regression model after removing only the given variable.
  2. bBased on the prognostic regression model after sequentially removing the variables in the stated order (1 = first, 2 = second, ..).
  3. cTreated as transformed continuous variables (see text for details). The remaining variables are categorical.
  4. dValues represent the difference in fit statistics between the model with thickness only cand the model including thickness cand the shown variable.