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Calibration failed before recalculation, but was acceptable afterwards.
Calibration failed for one system in the validation cohort.
Calibration failed with a goodness-of-fit test, χ=30.7, p=0.0007.
When recalculating all four scores, both discriminatory power and calibration improved, except for the Froom score, 7 where calibration failed.
Recalculating the Froom score, 7 we achieved excellent AUROCs in both cohorts, but calibration failed in the validation cohort (figure 2 and table 3).
After recalculation, however, calibration failed in the Froom score, 7 a system for which we could not test calibration using the original formula.
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For example a model evaluate on a large dataset with good calibration can fail the Hosmer-Lemeshow test, whilst a model validated on a small dataset with poor calibration can pass the Hosmer-Lemeshow test.
On the other hand, even a model with very good, though not perfect, calibration will fail the test in case of a sufficiently large sample.
The FA model failed calibration because of the presence of 4 controls with FA-based risk scores above the 90th percentile, highly predictive of case status.
Thus, the Loekito score showed excellent discriminatory power but failed calibration.
It was forwarded that the model yielded plausible results for runoff and sediment yield dynamics without the need of calibration, although the model failed to reproduce the shape of the hydrograph and the total discharge of several individual rainstorm events, hence the simulation capabilities are not yet considered sufficient for decision-making purposes for land management.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com