Table 3.
Results sensitivity analysis
| Scenario 8: (1) Age, (2) gender missing |
Imputation methods | MSE of the LP (% difference to M-Imp) |
C-index | CITL | Calibration slope |
|---|---|---|---|---|---|
| Apparent performance (reference) | 0.7051 | −0.0001 | 0.9999 | ||
|
Simulation 1 Local data (for informing imputation) |
M-Imp | 0.7438 | 0.6063 | 0.1958 | 0.8225 |
| JMI | 0.6373 (−14.32%) | 0.6223 | 0.1616 | 0.8052 | |
| JMIaux | 0.4517 (−39.26%) | 0.6931 | 0.0794 | 1.0828 | |
|
Simulation 2 External data (for informing imputation) |
M-Imp | 0.8334 | 0.6064 | −0.1037 | 0.8230 |
| JMI | 0.7963 (−4.45%) | 0.6116 | −0.2221 | 0.5769 | |
| JMIaux | 0.7018 (−15.79%) | 0.6721 | −0.3649 | 0.8453 | |
|
Simulation 3 External data with 1.500 local patients |
M-Imp | 0.792383 | 0.6107 | −0.0205 | 0.8429 |
| JMI | 0.7252996 (−9.25%) | 0.6131 | −0.0659 | 0.6480 | |
| JMIaux | 0.5739753 (−38.05% | 0.6856 | −0.1451 | 0.9654 |
CITL, calibration in the large; JMI, joint modelling imputation; JMI+, joint modelling imputation with auxiliary variables; LP, linear predictor; M-Imp, mean imputation; MSE, mean squared error.