Evidence-Based Diagnosis. An Introduction to Clinical Epidemiology

1. Introduction: Understanding Diagnosis and Evidence-Based Diagnosis
2. Dichotomous Tests
3. Multilevel and Continuous Tests
4. Critical Appraisal of Studies of Diagnostic Test Accuracy
5. Reliability and Measurement Error
6. Risk Predictions
7. Multiple Tests and Multivariable Decision Rules
8. Quantifying Treatment Effects Using Randomized Trials
9. Alternatives to Randomized Trials for Estimating Treatment Effects
10. Screening Tests
11. Understanding P-Values and Confidence Intervals
12. Challenges for Evidence-Based Diagnosis
Problems and Answers
Index

Medicine is becoming increasingly reliant on diagnostic, prognostic and screening tests for the successful treatment of patients. With new tests being developed all the time, a more informed understanding of the benefits and drawbacks of these tests is crucial. Providing readers with the tools needed to evaluate and interpret these tests, numerous real-world examples demonstrate the practical application and relevance of the material. The mathematics involved are rigorously explained using simple and informative language. Topics covered include the diagnostic process, reliability and accuracy of tests, and quantifying treatment benefits using randomized trials, amongst others. Engaging illustrations act as visual representations of the concepts discussed in the book, complementing the textual explanation. Based on decades of experience teaching in a clinical research training program, this fully updated second edition is an essential guide for anyone looking to select, develop or market medical tests.

• Real-word examples demonstrate the practical application and relevance of the material, holding readers' interests · Discussions are mathematically rigorous yet use simple, informal language, enabling readers to easily understand and appreciate the theory underlying the evaluation of tests and treatments · Engaging illustrations help make abstract concepts more concrete, complementing the textual explanations and adding to the fun of reading the book.

Thomas B. Newman, University of California, San Francisco
Thomas B. Newman is the Professor Emeritus of Epidemiology & Biostatistics and Pediatrics at the University of California San Francisco, USA.

Michael A. Kohn, University of California, San Francisco
Michael A. Kohn is a Professor of Epidemiology & Biostatistics at the University of California San Francisco, USA.