Survival of the different risk groups.

<p>Survival of the different risk groups.</p

Cut points of the predictive model and their indicators of validity, yield and usefulness.

<p>PPV, positive predictive value; NPV, negative predictive value; PLR, positive likelihood ratio test; NLR, negative likelihood ratio test; CI, confidence interval.</p><p>Cut points of the predictive model and their indicators of validity, yield and usefulness.</p

Area under the ROC curve of the predictive model.

<p>ROC, receiver operating characteristic; AUC, area under the curve; CI, confidence interval.</p

Estimated calibration index values according to the number of events in the sample.

<p>ECI, estimated calibration index.</p

Sample size calculation to externally validate scoring systems based on logistic regression models

<div><p>Background</p><p>A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence). Scoring systems based on binary logistic regression models are a specific type of predictive model.</p><p>Objective</p><p>The aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study.</p><p>Methods</p><p>The algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index) were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units.</p><p>Results</p><p>In the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature.</p><p>Conclusion</p><p>An algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases.</p></div

Smooth calibration plots (linear splines) for several sample sizes.

<p>The dashed lines denote the confidence intervals. The central line denotes the perfect prediction.</p

Analysis of mortality in cardiac arrest attended by the Emergency Medical Services in the province of Alicante in 2013.

<p>Analysis of mortality in cardiac arrest attended by the Emergency Medical Services in the province of Alicante in 2013.</p

Atypical hemoglobin A values (%) according to maternal origin and gestational age for male gender.

<p>Atypical hemoglobin A values (%) according to maternal origin and gestational age for male gender.</p

ROC curve multivariate logistic regression model.

<p>AUC, area under the ROC curve; CI, confidence interval.</p

Atypical hemoglobin A values (%) according to maternal origin and gestational age for female gender.

<p>Atypical hemoglobin A values (%) according to maternal origin and gestational age for female gender.</p