Accommodating Maternal Age in CRIB Scale: Quantifying the Effect on the Classification

  • Maria Filipa Mourão
  • Ana C. Braga
  • Pedro Nuno Oliveira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8581)


Receiver operating characteristic (ROC) curves are a well-accepted measure of accuracy of diagnostic tests using in continuous or ordinal markers. Based on the notion of using a threshold to classify subjects as positive (diseased) or negative (no diseased), a ROC curve is a plot of the true positive fraction (TPF) versus the false positive fraction (FPF)for all possible cut points. Thus, it describes the whole range of possible operating characteristic for the test and hence its inherent capacity for distinguish between two status. The clinical severity scale CRIB - Clinical Risk Index for Babies, emerged in 1993 to predict the mortality of newborn at less than 32 weeks of gestation and very low birth weight (< 1500gr) [4]. In previous work of Braga [3] this index was reported as showing a good performance in assessing risk of death for babies with very low birth weight (less than 1500 g weight). However, in some situations, the performance of the diagnostic test, the ROC curve itself and the Area Under the Curve(AUC) can be strongly influenced by the presence of covariates, whether continuous or categorical [5], [32], [32]. The World Health Organization and the Ministry of Health, defined as ”late pregnancy” that thus occurs in women over 35 years. In this work, using the conditional ROC curve, we analyze the effect of one covariate, maternal age, on the ROC curve that representing the diagnostic test performance. We chose two age status, less than 35 years old and equal or greater than 35 years old, to verify the effects on the discriminating power of CRIB scale, in the process classification using R and STATA software.


Conditional ROC (Receiver Operating Characteristic) curve CRIB (Clinical Risk Index for Babies) maternal age AUC (Area Under the Curve) 


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  1. 1.
    Metz, C.E.: Basic principles of ROC analysis. Seminars in Nuclear Medicine 8, 183–298Google Scholar
  2. 2.
    Swets, J.A., Pickett, R.M.: Evaluating of Diagnostic System: Methods from Signal Detetion Theory. Academic Press, New YorkGoogle Scholar
  3. 3.
    Braga, A.C., Oliveira, P., Gomes, A.: Avaliação do risco de morte em recém-nascidos de muito baixo peso: uma comparação de índices de risco baseada em curvas ROC. IV Congresso Anual da Sociedade Portuguesa de Estatística. Editores: Luísa Canto e Castro, Dinis Pestana, Rita Vasconcelos, Isabel Fraga Alves. Edições SalamandraGoogle Scholar
  4. 4.
    Dorling, J.S., Field, D.J., Manketelow, B.: Neonatal disease severity scoring systems. Arch. Dis. Child. Fetal Neonatal 90, F11–F16Google Scholar
  5. 5.
    López-de-Ullibarri, I., Cao, R., Cardaso-Suárez, C., Lado, M.J.: Nonparametric estimation of conditional ROC curves: application to discrimination tasks in computerized detection of early breast cancer. Computational Statistics & Data Analysis 52(5), 2623–2631Google Scholar
  6. 6.
    Metz, C.E.: Statistical Analysis of ROC Data in Evaluating Diagnostic Performance. Multiple Regression Analysis: Application in Health Sciences. American Institute of Physics 13, 365–384Google Scholar
  7. 7.
    Friede, A., Baldwin, W., Rhodes, P.H., Buehler, J.W., Strauss, L.T., Smith, J.C., Hogue, C.J.R.: Young Maternal Age and Infant Mortality: The role of low birth weight. Public Health Report 102(2) (March-April)Google Scholar
  8. 8.
    Friede, A., Baldwin, W., Rhodes, P.H., et al.: Older maternal age and infant mortality in the United States. Obstet. Gynecol. 72, 1527Google Scholar
  9. 9.
    Aras, R.Y.: Is maternal age risk factor for low birth weight? Archives of Medicine and Health Sciences 1(1) (January-June)Google Scholar
  10. 10.
    Cochran, W.G., Bliss, C.I.: Discriminant functions with covariance. Ann. Math. Statist. 19(2), 151–291Google Scholar
  11. 11.
    Cochran, W.G.: Comparison of two methods of handling covariates in discriminant analysis. Annals of the Institute of Statistical Mathematics 16, 43–53Google Scholar
  12. 12.
    Lachenbrush, P.A.: Covariance adjusted discriminant functions. Annals of the Institute of Statistical Mathematics 29, 247–257Google Scholar
  13. 13.
    Rao, C.R.: On some problems arising out of discrimination of multiple characters. The Indian Journal of Statistics 9, 343–366Google Scholar
  14. 14.
    McLachlan, G.J.: Discriminant analysis and pattern recognition. Wiley, New YorkGoogle Scholar
  15. 15.
    Guttman, I., Johnson, R.A., Bhattacharayya, G.K., Reiser, B.: Confidence limits for stress-strenght models with explanatory variables. Technometrics 30(2), 161–168Google Scholar
  16. 16.
    Tolsteson, A.N., Begg, C.B.: A general regression methodology for ROC curve estimation. Med. Decision Making 8(3), 204–215Google Scholar
  17. 17.
    Smith, D.J., Tompson, T.J.: Correcting for confounding in analising receiver operating characteristic curves. Biometrical Journal 38, 357–863Google Scholar
  18. 18.
    Pepe, M.S.: A regression modelling framework for receiver operating characteristic curve in medical diagnostic testing. Biometrika 84, 595–608Google Scholar
  19. 19.
    Pepe, M.S.: Three approaches to regression analysis of receiver operating characteristic curves for continuous tests results. Biometrics 54, 124–135Google Scholar
  20. 20.
    Pepe, M.S.: An interpretation for the ROC curve and inference using GLM procedures. Biometrics 56, 352–359Google Scholar
  21. 21.
    Faraggi, D.: Adjusting receiver operating characteristic curves and related indices for covariates. Journal of the Royal Statistical Society: Series D (The Statistician) 52(2), 179–192Google Scholar
  22. 22.
    Janes, H., Pepe, M.S.: Adjusting for covariates effects on classification accuracy using the covariate-adjusted ROC curve. UW Bioestatistics Workin Paper Series. Working paper 283,
  23. 23.
    Janes, H., Pepe, M.S.: Adjusting for covariates in studies of diagnostic, screening, or prognostic markers: An old concept in a new setting. UW Bioestatistics Working Paper Series. Working paper 310,
  24. 24.
    Dodd, L.E., Pepe, M.S.: Partial AUC estimation and regression. Biometrics 59(3), 614–623Google Scholar
  25. 25.
    Hanley, J.A., McNeil, B.J.: A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148, 839–843Google Scholar
  26. 26.
    Zheng, Y., Heagerty, P.J.: Semiparametric estimation of time-dependent ROC curves for longitudinal marker data. Biostatistics 4, 615–632Google Scholar
  27. 27.
    Cai, T.: Semiparametric ROC regression analysis with placement values. Biostatistics 5, 45–60Google Scholar
  28. 28.
    Alonzo, T.A., Pepe, M.S.: Distribution free ROC analysis using binary regression techniques. Biostatistics 3, 421–432Google Scholar
  29. 29.
    Pepe, M.S.: The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press, New YorkGoogle Scholar
  30. 30.
    Carolan, M., Frankowska, D.: Advanced maternal age and adverse perinatal outcome: A review of the evidence. Midwifery, doi:10.1016/j.midw.2010.07.006Google Scholar
  31. 31.
    Linda, J., Heffner, M.D.: Advanced Maternal Age How Old Is Too Old? The New England Journal of Medicine 351, 19Google Scholar
  32. 32.
    Rodriguez-Álvarez, M.X., Roca-Pardiñas, J., Cardaso-Suárez, C.: ROC curve and covariates: Extending induced methodology to the non-parametric framework. Statistical and Computing 21, 483–499Google Scholar
  33. 33.
    González-Manteiga, W., Pardo-Fernández, J.C., Keilegom, I.: ROC curves in Non-Parametric Location-Scale Regression Models. Scandinavian Journal of Statistics 38(1), 169–184Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Maria Filipa Mourão
    • 1
  • Ana C. Braga
    • 2
  • Pedro Nuno Oliveira
    • 3
  1. 1.School of Technology and Management-Polytechnic Institute of Viana do CasteloViana do CasteloPortugal
  2. 2.Department of Production and Systems Engineering, Algoritmi Research CentreUniversity of MinhoBragaPortugal
  3. 3.Biomedical Sciences Abel Salazar InstituteUniversity of PortoPortoPortugal

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