title: CRIB conditional on gender: nonparametric ROC curve.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  The challenge is to determine whether gender discriminates between dead and surviving babies.
  
  
  CONCLUSIONS
  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.
authors.forename: Maria Filipa
authors.initials: MF
authors.lastname: Mourão
authors.forename: Ana Cristina
authors.initials: AC
authors.lastname: Braga
authors.forename: Pedro Nuno
authors.initials: PN
authors.lastname: Oliveira
mesh_terms: D015982:Bias; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D015233:Models, Statistical; D012372:ROC Curve; D012307:Risk Factors; D012737:Sex Factors