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CRIB conditional on gender: nonparametric ROC curve

CRIB conditional on gender: nonparametric ROC curve Purpose – The purpose of this paper is to use the kernel method to produce a smoothed receiver operating characteristic (ROC) curve and show how baby gender can influence Clinical Risk Index for Babies (CRIB) scale according to survival risks. Design/methodology/approach – To obtain the ROC curve, conditioned by covariates, two methods may be followed: first, indirect adjustment, in which the covariate is first modeled within groups and then by generating a modified distribution curve; second, direct smoothing in which covariate effects is modeled within the ROC curve itself. To verify if new-born gender and weight affects the classification according to the CRIB scale, the authors use the direct method. The authors sampled 160 Portuguese babies. Findings – The smoothing applied to the ROC curves indicates that the curve's original shape does not change when a bandwidth h =0.1 is used. Furthermore, gender seems to be a significant covariate in predicting baby deaths. A higher value was obtained for the area under curve (AUC) when conditional on female babies. Practical implications – The challenge is to determine whether gender discriminates between dead and surviving babies. Originality/value – The authors constructed empirical ROC curves for CRIB data and empirical ROC curves conditioned on gender. The authors calculate the corresponding AUC and tested the difference between them. The authors also constructed smooth ROC curves for two approaches. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Health Care Quality Assurance Emerald Publishing

CRIB conditional on gender: nonparametric ROC curve

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0952-6862
DOI
10.1108/IJHCQA-04-2013-0047
Publisher site
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Abstract

Purpose – The purpose of this paper is to use the kernel method to produce a smoothed receiver operating characteristic (ROC) curve and show how baby gender can influence Clinical Risk Index for Babies (CRIB) scale according to survival risks. Design/methodology/approach – To obtain the ROC curve, conditioned by covariates, two methods may be followed: first, indirect adjustment, in which the covariate is first modeled within groups and then by generating a modified distribution curve; second, direct smoothing in which covariate effects is modeled within the ROC curve itself. To verify if new-born gender and weight affects the classification according to the CRIB scale, the authors use the direct method. The authors sampled 160 Portuguese babies. Findings – The smoothing applied to the ROC curves indicates that the curve's original shape does not change when a bandwidth h =0.1 is used. Furthermore, gender seems to be a significant covariate in predicting baby deaths. A higher value was obtained for the area under curve (AUC) when conditional on female babies. Practical implications – The challenge is to determine whether gender discriminates between dead and surviving babies. Originality/value – The authors constructed empirical ROC curves for CRIB data and empirical ROC curves conditioned on gender. The authors calculate the corresponding AUC and tested the difference between them. The authors also constructed smooth ROC curves for two approaches.

Journal

International Journal of Health Care Quality Assurance – Emerald Publishing

Published: Oct 7, 2014

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