Evidence-Based Diagnosis

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Cambridge University Press, Feb 16, 2009 - Medical
Evidence-Based Diagnosis explains diagnostic, screening, and prognostic tests in clinical medicine. The authors' approach is based on many years of experience teaching physicians in a clinical research training program. Although needing only a minimum of mathematics, the quantitative discussions in this book are deeper and more rigorous than in most introductory texts. The book includes numerous worked examples and 60 problems (with answers) based on real clinical situations and journal articles. This book is a great choice for anyone looking to select, develop, or apply medical tests. Topics covered include: the diagnostic process; test reliability and accuracy; testing and treatment thresholds; critical appraisal of studies of diagnostic, screening and prognostic tests; test independence and methods of combining tests; quantifying treatment benefits using randomized trials and observational studies; Bayesian interpretation of P values and confidence intervals; challenges for evidence-based diagnosis; likelihood ratios and ROC curves.

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This well-written textbook is the ideal synopsis for clinicians, researchers, editors, and policy-makers who are engaged in diagnostic and prognostic decision making. The authors provide a concise, precisely referenced, and pragmatic discussion of diagnostic science, including test reliability and accuracy, interval likelihood ratios, and test-treatment thresholds. Problem sets based upon published studies are included with helpful and appropriately detailed answer keys. Readers will undoubtedly reference this textbook repeatedly as new diagnostic and prognostic research emerges in coming years. 


understanding diagnosis and diagnostic testing
2 Reliability and measurement error
3 Dichotomous tests
4 Multilevel and continuous tests
5 Critical appraisal of studies of diagnostic tests
6 Screening tests
7 Prognostic tests and studies
8 Multiple tests and multivariable decision rules
9 Quantifying treatment effects using randomized trials
10 Alternatives to randomized trials for estimating treatment effects
11 Understanding Pvalues and confidence intervals
12 Challenges for evidencebased diagnosis
Answers to problems

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About the author (2009)

Thomas B. Newman, currently Chief of the Division of Clinical Epidemiology and Professor of Epidemiology and Biostatistics and Pediatrics at the University of California, San Francisco, previously served as Associate Director of the UCSF/Stanford Robert Wood Johnson Clinical Scholars Program and Associate Professor in the Department of Laboratory Medicine at UCSF. He is a co-author of Designing Clinical Research and a currently practicing pediatrician.

Michael A. Kohn is Associate Professor of Epidemiology and Biostatistics at the University of California, San Francisco, where he teaches clinical epidemiology and evidence-based medicine. He is also an emergency physician with more than 20 years of clinical experience, currently practising at Mills-Peninsula Medical Center in Burlingame, California.

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