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The Statistical Evaluation of Medical Tests for Classification and Prediction (Oxford Statistical Science Series), by Margaret Sullivan Pe
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This book describes statistical concepts and techniques for evaluating medical diagnostic tests and biomarkers for detecting disease. More generally, the techniques pertain to the statistical classification problem for predicting a dichotomous outcome. Measures for quantifying test accuracy are described including sensitivity, specificity, predictive values, diagnostic likelihood ratios and the Receiver Operating Characteristic Curve that is commonly used for continuous and ordinal valued tests. Statistical procedures are presented for estimating and comparing them. Regression frameworks for assessing factors that influence test accuracy and for comparing tests while adjusting for such factors are presented. This book presents many worked examples of real data and should be of interest to practicing statisticians or quantitative researchers involved in the development of tests for classification or prediction in medicine.
- Sales Rank: #592320 in Books
- Published on: 2004-12-16
- Original language: English
- Number of items: 1
- Dimensions: 6.20" h x .60" w x 9.10" l, 1.10 pounds
- Binding: Paperback
- 318 pages
Review
"...[T]his is a timely book that appears to cover a gap in the existing literature...This book is well written."--Journal of the American Statistical Association
About the Author
Margaret Sullivan Pepe is a Professor of Biostatistics, University of Washington; Fred Hutchinson Cancer Research Center, Washington, U.S.A. . Margaret Sullivan Pepe is a Professor of Biostatistics, University of Washington; Fred Hutchinson Cancer Research Center, Washington, U.S.A. .
Most helpful customer reviews
0 of 0 people found the following review helpful.
Good strong introduction to understand how to use ROC curve to analyze medical tests.
By W. YIP
Nice formal introduction to use ROC curve to evaluate medical tests diagnostics. It went through all the basic concepts such as sensitivity and specificity and the different measurements such as AUC and the binomal distribution to quantify the ROC curve. Unfortunatley, I was looking for one particular topic that the book did not cover - introduction of "reader" for the test diagnostics. It makes all the statistics very complicated as a result and an area which is not being covered well.
37 of 37 people found the following review helpful.
well written book on an important topic
By Michael R. Chernick
With the increase in cancer trials both in medical research and the pharmaceutical industry, medical diagnostic tests including medical imaging evaluations are being used more and more. There is a need for adjudication when radiologists disagree on a diagnosis and some standard statistical measures from other fields are finding new application.
This book provides a thorough background on the subject, the methodology and the applications. It is very clearly written and not overly technical. The methodology is the classical frequentist approach to statistical inference. Recently, Lyle Broemeling has published a book on this topic that takes exclusively the Bayesian approach and explains the Bayesian approach for those who are not acquainted with it. When dealing with predictions based on evolving data the Bayesian approach might be more natural. This would be the case in a clinical trial where the data is reviewed sequentially.
A particularly important part of diagnostic accuracy analysis is the area under the ROC curve. Pepe does an excellent job of covering that topic. This text is referenced often in Broemeling's book since it provides an excellent explanation of the same topics using the frequentist approach that he covers with the Bayesian approach.
6 of 9 people found the following review helpful.
The book is excellent, its rendering into Kindle format atrocious
By R. O. Doehring
Pepe's text on the statistical evaluation of diagnostic tests is perhaps the most readable and approachable in the field. Being an avid user of my iPad with Kindle app, I bought the Kindle version of the book. I was extremely disappointed to find that not only did the Kindle rendition make the page numbering of the Contents and Index meaningless, but there is considerable coruption of mathematical notation, in particular sub- and superscripts.
As a result of my frustration with using the Kindle version, I have bought the paperback - which is as excellent as I expected it to be.
Until Amazon has improved the technology to the extent that a book such as this is faithfully rendered in Kindle, I think it is improper to offer the Kindle version for sale, and certainly when so high a price is asked!
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