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And as it turns out, they are perfectly correct -- optimism doesn't work for them.
Moreover, when studies do correct for optimism, various methods are used.
However, many studies do not use such methods, and those that do correct for optimism use diverse methods, some of which are known to be biased.
To correct for optimism bias in the C statistic value (that is, over-fitting to a specific sample), we undertook internal validation using 100 bootstrap samples.
For validation, we used a bootstrap-based method to correct for optimism in our estimates of incidence in each of the risk strata.
The apparent AUC-values of each model were corrected for optimism using 10 × 10 cross-validation.
The aim of the present study was to compare these approaches to correcting for optimism.
'Internally validated performance' corrects for optimism in the apparent performance to yield approximately unbiased estimates of future model performance.
This led to an NRI of 13.4% (P = 0.006), which decreased to 6.2% after correcting for optimism using a bootstrap method.
To report model performance characteristics in tables we chose the predicted probability threshold that resulted in 90% specificity before correcting for optimism (see below).
To determine calibration and discrimination of the model corrected for optimism, we generated 1000 bootstrap samples, which were used as training sets in a bootstrap validation procedure.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com