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


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.

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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).

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Correspondence to Zhen Chen.

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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

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