Table 2 Updating methods for multinomial logistic regression models with the numbers of parameters that are estimated for updating in general and in the case study
From: Validation and updating of risk models based on multinomial logistic regression
| Category | Method and description | Number of parameters |
|---|---|---|
| (General = case study) | ||
| Original | 0—no adjustments | 0 = 0 |
| Recalibration | 1—intercept recalibration: adjust intercepts | (k − 1) = 2 |
| 2—logistic recalibration: adjust intercepts and slopes | k × (k − 1) = 6 | |
| 3—refitting: re-estimation of individual coefficients | (q + 1) × (k − 1) = 8 | |
| Revision | 4—penalized refitting using recalibrated coefficients from method 2 as offset | (k + q + 1) × (k − 1) = 14 |
| 5—refitting including functional form: method 3, but hCGr modeled with rcs | (q′ + 1) × (k − 1) = 8 | |
| Extension | 6—extension: similar to method 3 but log(progesterone) added | (q + m + 1) × (k − 1) = 10 |
| 7—penalized extension: similar to method 5 but log(progesterone) added | (k + q + m + 1) × (k − 1) = 16 |