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Internal bootstrap validation, bias-corrected 95% confidence intervals for hazard ratios in the final model, and bootstrap optimism corrected c-index were calculated using 1000 re-samples (Chen and George, 1985).
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The discriminative ability was measured by the so-called c-index, which is equal to the area under the curve (AUC) for logistic models and was corrected, by using bootstrapping, for optimism in the original sample [ 33].
The sensitivity and specificity of the resulting classification rule were estimated via bootstrap adjustment for optimism [ 27].
Using bootstrap validation, the optimism-corrected ROC AUC was 0.73 [0.65–0.82], which represents a good predictive ability of the model in future patients [18].
To assess model fit and optimism, bootstrapped estimates of the Nagelkerke R and its SE will be compared with the original model estimates.
‡Corrected for optimism using bootstrap resampling [ 22].
Both the DHAKA Dehydration Score and DHAKA Dehydration Tree had significant positive and negative likelihood ratios, 90% inter-rater agreement, and modest optimism on bootstrap analysis.
We corrected model performance characteristics for optimism using bootstrap resampling, which has been recommended as a better method for internal validation than a split-sample approach [ 22].
To perform an internal validation, we used a bootstrapping procedure (N = 500 bootstrap samples) to evaluate optimism of the parameters of models and calculated the odds ratio (OR) associated with inappropriate treatment and its 95% confidence interval (CI).
The prediction model was internally validated using bootstrap resampling, assessing its optimism-corrected discrimination and calibration.
To avoid this bias we used for all performance measures optimism-corrected bootstrap validation with 1,000 bootstrap replications [ 42, 43].
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