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.
A rigorous yet engaging guide to understanding and choosing a range of diagnostic and prognostic tests. Topics explored include the diagnostic process, the reliability and accuracy of different tests and quantifying treatment benefits using randomized trails. Numerous worked examples and problems based on real situations support readers' learning.
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
Explains the mathematics involved in understanding and choosing an array of diagnostic and prognostic tests, in order to improve treatment.