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For each model, we calculated the same indices for assessing model performance [19] as in the derivation model.
Specifically, we used the coefficients estimated from the derivation model to predict the log-odds of mortality in the validation cohorts.
All variables in the derivation model were retained in the bootstrapped model (Table 4).
We used the bootstrapping method to derive robust estimates of the standard errors of the odds ratios of the variables in the derivation model.
Based on the derivation model, the probability for having breast cancer in the validation cohort was used to divide subjects into deciles.
Second, the model requires validation in an external dataset; however, an internal validation with bootstrapping was applied to the derivation model.
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We applied each of the four derived models to its corresponding validation cohort, using the coefficients from the derivation models.
The AUC for the derivation models ranged from 0.64 to 0.73 (Additional file 1).
The results of the multivariate derivation model are expressed as odds ratios and displayed in Table 3.
The resultant derivation model's discrimination and calibration were assessed using the receiver operator characteristic (ROC) curve and Hosmer-Lemeshow test, respectively.
The theoretical derivation, model simulation, and real data applications are demonstrated in this paper.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com