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A note on modeling placement values in the analysis of receiver operating characteristic curves

A note on modeling placement values in the analysis of receiver operating characteristic curves Recent advances in receiver operating characteristic (ROC) curve analyses advocate modeling of placement value (PV), a quantity that measures the position of diseased test scores relative to the healthy population. Compared to traditional approaches, this PV-based alternative works directly with ROC curves and is attractive when assessing covariate effects on, or incorporating a priori constraints of, ROC curves. Several distributions can be used to model the PV, yet little guidelines exist in the literature on which to use. Through extensive simulation studies, we investigate several parametric models for PV when data are generated from a variety of mechanisms. We discuss the pros and cons of each of these models and illustrate their applications with data from a study of prenatal ultrasound examinations and large-for-gestational age birth. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

A note on modeling placement values in the analysis of receiver operating characteristic curves

Chen, Zhen; Ghosal, Soutik
Biostatistics & Epidemiology , Volume 5 (2): 16 – Jul 3, 2021
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A note on modeling placement values in the analysis of receiver operating characteristic curves

Abstract

Recent advances in receiver operating characteristic (ROC) curve analyses advocate modeling of placement value (PV), a quantity that measures the position of diseased test scores relative to the healthy population. Compared to traditional approaches, this PV-based alternative works directly with ROC curves and is attractive when assessing covariate effects on, or incorporating a priori constraints of, ROC curves. Several distributions can be used to model the PV, yet little guidelines exist...
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Publisher
Taylor & Francis
Copyright
© 2020 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2020.1737794
Publisher site
See Article on Publisher Site

Abstract

Recent advances in receiver operating characteristic (ROC) curve analyses advocate modeling of placement value (PV), a quantity that measures the position of diseased test scores relative to the healthy population. Compared to traditional approaches, this PV-based alternative works directly with ROC curves and is attractive when assessing covariate effects on, or incorporating a priori constraints of, ROC curves. Several distributions can be used to model the PV, yet little guidelines exist in the literature on which to use. Through extensive simulation studies, we investigate several parametric models for PV when data are generated from a variety of mechanisms. We discuss the pros and cons of each of these models and illustrate their applications with data from a study of prenatal ultrasound examinations and large-for-gestational age birth.

Journal

Biostatistics & Epidemiology – Taylor & Francis

Published: Jul 3, 2021

Keywords: AUC; ROC; diagnostic accuracy; large for gestational age; beta regression

References

  • Distribution-free ROC analysis using binary regression techniques
  • Semi-parametric ROC regression analysis with placement values
  • Semiparametric regression for the area under the receiver operating characteristic curve
  • The analysis of placement values for evaluating discriminatory measures
  • Sampling variability of nonparametric estimates of the areas under receiver operating characteristic curves: an update
  • Beta regression for modeling a covariate adjusted ROC
  • Comparative study of ROC regression techniques – applications for the computer-aided diagnostic system in breast cancer detection
  • ROC methodology in radiologic imaging
  • Semiparametric estimation of regression quantiles with application to standardizing weight for height and age in us children
  • A regression modelling framework for receiver oerating characteristic curves in medical diagnostic testing
  • Proper receiver operating characteristic analysis: the bigamma model
  • Estimating the area under ROC curve when the fitted binormal curves demonstrate improper shape
  • Multivariate binormal mixtures for semi-parametric inference on ROC curves
  • Prepregnancy risk-factors of small-for-gestational-age births among parous women in Scandinavia
  • Estimation of fetal weight with the use of head, body, and femur measurements -- a prospective-study
  • Percentiles of birth weight and crown-heel length for single live births
  • A regression approach to ROC surface, with applications to Alzheimer's disease
  • Measuring and estimating diagnostic accuracy when there are three ordinal diagnostic groups
  • Bayesian bootstrap inference for the receiver operating characteristic surface
  • The robustness of the “binormal” assumptions used in fitting roc curves
  • The use of the “binormal” model for parametric roc analysis of quantitative diagnostic tests
  • Indices of discrimination or diagnostic accuracy: their ROCs and implied models