10% off all books and free delivery over £40
Buy from our bookstore and 25% of the cover price will be given to a school of your choice to buy more books. *15% of eBooks.

Advanced Bayesian Methods for Medical Test Accuracy

View All Editions (1)

The selected edition of this book is not available to buy right now.
Add To Wishlist
Write A Review

About

Advanced Bayesian Methods for Medical Test Accuracy Synopsis

Useful in many areas of medicine and biology, Bayesian methods are particularly attractive tools for the design of clinical trials and diagnostic tests, which are based on established information, usually from related previous studies. Advanced Bayesian Methods for Medical Test Accuracy begins with a review of the usual measures such as specificity, sensitivity, positive and negative predictive value, and the area under the ROC curve. Then the scope expands to cover the more advanced topics of verification bias, diagnostic tests with imperfect gold standards, and those for which no gold standard is available. Promoting accuracy and efficiency of clinical trials, tests, and the diagnostic process, this book: Enables the user to efficiently apply prior information via a WinBUGS package Presents many ideas for the first time and goes far beyond the two standard references Integrates reader agreement with different modalities—X-ray, CT Scanners, and more—to study their effect on medical test accuracy Provides practical chapter-end problems Useful for graduate students and consulting statisticians working in the various areas of diagnostic medicine and study design, this practical resource introduces the fundamentals of programming and executing BUGS, giving readers the tools and experience to successfully analyze studies for medical test accuracy.

About This Edition

ISBN: 9781439838785
Publication date:
Author: Lyle D Broemeling and Associates Inc, USA Broemeling
Publisher: CRC Press Inc an imprint of Taylor & Francis Inc
Format: Hardback
Pagination: 488 pages
Series: Chapman & Hall/CRC Biostatistics Series
Genres: Probability and statistics
Medical diagnosis
Medical imaging