Discriminatory Capacity of Prenatal Ultrasound Measures for Large-for-Gestational-Age Birth: A Bayesian Approach to ROC Analysis Using Placement Values

Abstract

Predicting large fetuses at birth is of great interest to obstetricians. Using an NICHD Scandinavian Study that collected longitudinal ultrasound examination data during pregnancy, we estimate diagnostic accuracy parameters of estimated fetal weight (EFW) at various times during pregnancy in predicting large for gestational age. We adopt a placement value-based Bayesian regression model with random effects to estimate ROC curves. The use of placement value allows us to model covariate effects directly on the ROC curves, and the adoption of a Bayesian approach accommodates the a priori constraint that an ROC curve of EFW near delivery should dominate another further away. The proposed methodology is shown to perform better than some alternative approaches in simulations and its application to the Scandinavian Study data suggest that diagnostic accuracy of EFW can improve about 65% from week 17 to 37 of gestation.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 1.

    Albert PS (2012) A linear mixed model for predicting a binary event from longitudinal data under random effects misspecification. Stat Med 31(2):143–154

    MathSciNet  Article  Google Scholar 

  2. 2.

    Alonzo TA, Pepe MS (2002) Distribution-free ROC analysis using binary regression techniques. Biostatistics 3(3):421–432

    Article  Google Scholar 

  3. 3.

    Bakketeig LS, Jacobsen G, Hoffman HJ, Lindmark G, Bergsjø P, Molne K, Rødsten J (1993) Pre-pregnancy risk factors of small-for-gestational age births among parous women in Scandinavia. Acta Obstet Gynecol Scand 72(4):273–279

    Article  Google Scholar 

  4. 4.

    Ben-Haroush A, Chen R, Hadar E, Hod M, Yogev Y (2007a) Accuracy of a single fetal weight estimation at 29–34 weeks in diabetic pregnancies: can it predict large-for-gestational-age infants at term? Am J Obstet Gynecol 197(5):497.e1-497.e6

    Article  Google Scholar 

  5. 5.

    Ben-Haroush A, Yogev Y, Hod M, Bar J (2007b) Predictive value of a single early fetal weight estimate in normal pregnancies. Eur J Obst Gynecol Reprod Biol 130(2):187–192

    Article  Google Scholar 

  6. 6.

    Bryant DR, Zador I, Landwehr JB, Wolfe HM (1997) Limited clinical utility of midtrimester fetal morphometric percentile rankings in screening for birth weight abnormalities. Eur J Obst Gynecol Reprod Biol 177(4):859–863

    Google Scholar 

  7. 7.

    Cai T (2004) Semi-parametric ROC regression analysis with placement values. Biostatistics 5(1):45–60

    Article  Google Scholar 

  8. 8.

    Cai T, Moskowitz CS (2004) Semi-parametric estimation of the binormal ROC curve for a continuous diagnostic test. Biostatistics 5(4):573–586

    Article  Google Scholar 

  9. 9.

    Cai T, Pepe MS (2002) Semiparametric receiver operating characteristic analysis to evaluate biomarkers for disease. J Am Stat Assoc 97(460):1099–1107

    MathSciNet  Article  Google Scholar 

  10. 10.

    Chen Z, Ghosal S (2020) A note on modeling placement values in the analysis of receiver operating characteristic curves. Biostat Epidemiol 60:1–16. https://doi.org/10.1080/24709360.2020.1737794

    Article  Google Scholar 

  11. 11.

    Chen Z, Hwang BS (2019) A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints. Biometrics 75(2):539–550

    MathSciNet  Article  Google Scholar 

  12. 12.

    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837–845

    Article  Google Scholar 

  13. 13.

    Dodd LE, Pepe MS (2003) Partial AUC estimation and regression. Biometrics 59(3):614–623

    MathSciNet  Article  Google Scholar 

  14. 14.

    Esakoff TF, Cheng YW, Sparks TN, Caughey AB (2009) The association between birthweight 4000 g or greater and perinatal outcomes in patients with and without gestational diabetes mellitus. Am J Obstet Gynecol 200(6):672.e1-672.e4

    Article  Google Scholar 

  15. 15.

    Foster JC, Liu D, Albert PS, Liu A (2017) Identifying subgroups of enhanced predictive accuracy from longitudinal biomarker data by using tree-based approaches: Applications to fetal growth. J R Stat Soc A 180(1):247–261

    MathSciNet  Article  Google Scholar 

  16. 16.

    Gail MH, Pfeiffer RM (2005) On criteria for evaluating models of absolute risk. Biostatistics 6(2):227–239

    Article  Google Scholar 

  17. 17.

    Gelman A, Rubin DB et al (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):457–472

    MATH  Google Scholar 

  18. 18.

    Gu J, Ghosal S, Roy A (2008) Bayesian bootstrap estimation of ROC curve. Stat Med 27(26):5407–5420

    MathSciNet  Article  Google Scholar 

  19. 19.

    Hadlock FP, Harrist R, Sharman RS, Deter RL, Park SK (1985) Estimation of fetal weight with the use of head, body, and femur measurements–a prospective study. Am J Obstet Gynecol 151(3):333–337

    Article  Google Scholar 

  20. 20.

    Hanley JA, Hajian-Tilaki KO (1997) Sampling variability of nonparametric estimates of the areas under receiver operating characteristic curves: an update. Acad Radiol 4(1):49–58

    Article  Google Scholar 

  21. 21.

    Hedriana HL, Moore TR (1994) A comparison of single versus multiple growth ultrasonographic examinations in predicting birth weight. Am J Obstet Gynecol 170(5):1600–1606

    Article  Google Scholar 

  22. 22.

    Hwang BS, Chen Z (2015) An integrated Bayesian nonparametric approach for stochastic and variability orders in ROC curve estimation: an application to endometriosis diagnosis. J Am Stat Assoc 110(511):923–934

    MathSciNet  Article  Google Scholar 

  23. 23.

    Inácio de Carvalho V, Rodríguez-Álvarez MX (2018) Bayesian nonparametric inference for the covariate-adjusted ROC curve. arXiv:1806.00473

  24. 24.

    Larsen T, Greisen G, Petersen S (1995) Prediction of birth weight by ultrasound-estimated fetal weight: a comparison between single and repeated estimates. Eur J Obst Gynecol Reprod Biol 60(1):37–40

    Article  Google Scholar 

  25. 25.

    Lin H, Zhou X-H, Li G (2012) A direct semiparametric receiver operating characteristic curve regression with unknown link and baseline functions. Statistica Sinica 22(4):1427–1456

    MathSciNet  MATH  Google Scholar 

  26. 26.

    Liu D, Albert PS (2014) Combination of longitudinal biomarkers in predicting binary events. Biostatistics 15(4):706–718

    Article  Google Scholar 

  27. 27.

    Metz CE (1986) ROC methodology in radiologic imaging. Invest Radiol 21(9):720–733

    Article  Google Scholar 

  28. 28.

    Pepe MS (1997) A regression modelling framework for receiver operating characteristic curves in medical diagnostic testing. Biometrika 84(3):595–608

    MathSciNet  Article  Google Scholar 

  29. 29.

    Pepe MS (2000) An interpretation for the ROC curve and inference using GLM procedures. Biometrics 56(2):352–359

    MathSciNet  Article  Google Scholar 

  30. 30.

    Pepe MS (2004) The statistical evaluation of medical tests for classification and prediction. Oxford University Press, Oxford

    MATH  Google Scholar 

  31. 31.

    Pepe MS, Cai T (2004) The analysis of placement values for evaluating discriminatory measures. Biometrics 60(2):528–535

    MathSciNet  Article  Google Scholar 

  32. 32.

    Pressman EK, Bienstock JL, Blakemore KJ, Martin SA, Callan NA (2000) Prediction of birth weight by ultrasound in the third trimester. Obst Gynecol 95(4):502–506

    Google Scholar 

  33. 33.

    Qin G, Zhou X-H (2006) Empirical likelihood inference for the area under the ROC curve. Biometrics 62(2):613–622

    MathSciNet  Article  Google Scholar 

  34. 34.

    Rubin DB (1981) The Bayesian bootstrap. Ann Stat 9(1):130–134

    MathSciNet  Article  Google Scholar 

  35. 35.

    Stanley S, Tubbs J (2018) Beta regression for modeling a covariate adjusted ROC. Sci J Appl Math Stat 6(4):110–118

    Article  Google Scholar 

  36. 36.

    Zhang J, Kim S, Grewal J, Albert PS (2012) Predicting large fetuses at birth: Do multiple ultrasound examinations and longitudinal statistical modelling improve prediction? Paediatr Perinat Epidemiol 26(3):199–207

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Intramural Research Program of Eunice Kennedy Shriver National Institute of Child Health and Human Development. This work utilized the computational resources of the NIH HPC Biowulf cluster. (http://hpc.nih.gov).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Zhen Chen.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ghosal, S., Chen, Z. Discriminatory Capacity of Prenatal Ultrasound Measures for Large-for-Gestational-Age Birth: A Bayesian Approach to ROC Analysis Using Placement Values. Stat Biosci (2021). https://doi.org/10.1007/s12561-021-09311-9

Download citation

Keywords

  • AUC
  • Estimated fetal weight
  • Obstetrics
  • Macrosomia
  • Diagnostic accuracy