Table 1 (abstract O31). Overview of the three approaches we distinguish to assess the incremental value of a new predictor and the incremental value of the D-dimer test for the prediction of deep venous thrombosis

1. Existing model

“...when the original model is used in the new dataset as originally developed?”

α + β₁lp (a)

vs.

α + β₁lp + β₂NP (b)

Refitting all coefficients of the model is not an option. An alternative is to improve the discrimination and calibration of the original model in your dataset using fractional polynomials, splines (‘), or simple recalibration for the linear predictor (lp) of the original model.

Note that α + β₁ in model (a) may differ from α + β₁ in model (b) due to adjustment for the new predictor.

ΔAUC

NRIe

NRIc

0.085 (-0.012 to 0.18)

0.084 (0.013 to 0.15)

0.64 (0.56 to 0.72)

2. Model revision

“...when the original model is optimally fit to the new dataset?”

α + β₁X₁ + β₂X₂ (a)

vs.

α + β₁X₁ + β₂X₂ + β₃NP (b)

Refit entire model with the same predictors (a) and add the new predictor to a model with the same predictors as in the original model (b).

Note that α + β_1,2,3 in model (a) may differ from α + β_1,2,3 in model (b) due to adjustment for the new predictor.

ΔAUC

NRIe

NRIc

0.082 (-0.012 to 0.18)

0.083 (0.0074 to 0.16)

0.61 (0.52 to 0.70)

3. New model development^*

“...when the new predictor is incorporated in the original model in the new dataset?”

α + β₁X₁ + β₃X₃ (a)

vs.

α + β₁X₁ + β₄NP (b)

Here β₂X₂ was removed from the existing model (a). After adding the new predictor (b) β₃X₃ was replaced by β₄NP

Refit entire model with the same predictors, but now allow predictor selection (a). Repeat this step, but now after adding the new predictor to the list of candidate predictors (b).

Note that model (b) may include different predictors than model (a), due to replacement of predictors in model (a) by the new predictor in model (b)

ΔAUC

NRIe

NRIc

0.080 (-0.015 to 0.17)

0.077 (0.0023 to 0.15)

0.59 (0.48 to 0.70)