Evidence-Based Diagnosis
An Introduction to Clinical Epidemiology

Explains the mathematics involved in understanding and choosing an array of diagnostic and prognostic tests, in order to improve treatment.

Thomas B. Newman (Author), Michael A. Kohn (Author)

9781108436717, Cambridge University Press

Paperback / softback, published 25 June 2020

405 pages
23.5 x 15.6 x 1.6 cm, 0.65 kg

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.

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

Subject Areas: Epidemiology & medical statistics [MBNS], Personal & public health [MBNH], Public health & preventive medicine [MBN]