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

Soutik Ghosal and Zhen Chen ()
Additional contact information
Soutik Ghosal: Eunice Kennedy Shriver National Institute of Child Health and Human Development
Zhen Chen: Eunice Kennedy Shriver National Institute of Child Health and Human Development

Statistics in Biosciences, 2022, vol. 14, issue 1, No 1, 22 pages

Abstract: 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.

Keywords: AUC; Estimated fetal weight; Obstetrics; Macrosomia; Diagnostic accuracy (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-021-09311-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stabio:v:14:y:2022:i:1:d:10.1007_s12561-021-09311-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561

DOI: 10.1007/s12561-021-09311-9

Access Statistics for this article

Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin

More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().