Udvidet returret til d. 31. januar 2025

Statistical Evaluation of Diagnostic Performance

- Topics in ROC Analysis

Bag om Statistical Evaluation of Diagnostic Performance

An important method for statistical validation is the receiver operating characteristic (ROC) analysis. This visual tool is used in a variety of clinical areas, including laboratory testing, epidemiology, radiology, and bioinformatics, for evaluating diagnostic tests. This book gives a historical overview of the empirical and nonparametric ROC method for continuous diagnostic and classification data. It introduces methods for estimating and comparing ROC curves based on diagnostic test results and covers both semiparametric and parametric models. The authors develop likelihood-based algorithms for estimating an ROC curve and its characteristics under these models. They also present methods for sample size calculations and Monte Carlo simulations. The text includes many real clinical examples, with R code provided for all of them.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9781439812228
  • Indbinding:
  • Hardback
  • Sideantal:
  • 246
  • Udgivet:
  • 27. juli 2011
  • Størrelse:
  • 179x236x21 mm.
  • Vægt:
  • 574 g.
  • BLACK WEEK
  Gratis fragt
Leveringstid: 8-11 hverdage
Forventet levering: 10. december 2024
Forlænget returret til d. 31. januar 2025

Beskrivelse af Statistical Evaluation of Diagnostic Performance

An important method for statistical validation is the receiver operating characteristic (ROC) analysis. This visual tool is used in a variety of clinical areas, including laboratory testing, epidemiology, radiology, and bioinformatics, for evaluating diagnostic tests. This book gives a historical overview of the empirical and nonparametric ROC method for continuous diagnostic and classification data. It introduces methods for estimating and comparing ROC curves based on diagnostic test results and covers both semiparametric and parametric models. The authors develop likelihood-based algorithms for estimating an ROC curve and its characteristics under these models. They also present methods for sample size calculations and Monte Carlo simulations. The text includes many real clinical examples, with R code provided for all of them.

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