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Autorenporträt
Harold C. Sox is Emeritus Professor of Medicine and of the Dartmouth Institute at Geisel School of Medicine at Dartmouth, USA. Michael C. Higgins is Adjunct Professor at the Stanford Center for Biomedical Informatics Research, Stanford University, USA. Douglas K. Owens is a general internist and Professor and Chair of the Department of Health Policy, School of Medicine, and Director of Stanford Health Policy, Freeman-Spogli Institute for International Studies, Stanford University, USA. Gillian Sanders Schmidler is Professor of Population Health Sciences and Medicine at Duke University and Deputy Director of the Duke-Margolis Institute for Health Policy, Durham, USA.
Inhaltsangabe
Foreword xi
Preface xiii
1 Introduction 1
1.1 How may I be thorough yet efficient when considering the possible causes of my patient's problems? 1
1.2 How do I characterize the information I have gathered during the medical interview and physical examination? 1
1.3 How do I interpret new diagnostic information? 3
1.4 How do I select the appropriate diagnostic test? 4
1.5 How do I choose among several risky treatment alternatives? 4
2 Differential diagnosis 5
2.1 An introduction 5
2.2 How clinicians make a diagnosis 5
2.3 The principles of hypothesis- driven differential diagnosis 8
2.4 An extended example 14
Bibliography 16
3 Probability: quantifying uncertainty 18
3.1 Uncertainty and probability in medicine 18
3.2 How to determine a probability 21
3.3 Sources of error in using personal experience to estimate the probability 23
3.4 The role of empirical evidence in quantifying uncertainty 30
3.5 Limitations of published studies of disease prevalence 35
3.6 Taking the special characteristics of the patient into account when determining probabilities 36
Bibliography 37
4 Interpreting new information: Bayes' theorem 38
4.1 Introduction 38
4.2 Conditional probability defined 40
4.3 Bayes' theorem 41
4.4 The odds ratio form of Bayes' theorem 45
4.5 Lessons to be learned from using Bayes' theorem 50
4.6 The assumptions of Bayes' theorem 52
4.7 Using Bayes' theorem to interpret a sequence of tests 54
4.8 Using Bayes' theorem when many diseases are under consideration 55
Bibliography 57
5 Measuring the accuracy of clinical findings 58
5.1 A language for describing test results 58
5.2 The measurement of diagnostic test performance 62
5.3 How to measure diagnostic test performance: a hypothetical example 67
5.4 Pitfalls of predictive value 69
5.5 How to perform a high quality study of diagnostic test performance 70
5.6 Spectrum bias in the measurement of test performance 74
5.7 When to be concerned about inaccurate measures of test performance 79
5.8 Test results as a continuous variable: the ROC curve 81
5.9 Combining data from studies of test performance: the systematic review and meta- analysis 87
A.5.1 Appendix: derivation of the method for using an ROC curve to choose the definition of an abnormal test result 89
Bibliography 91
6 Decision trees - representing the structure of a decision problem 93
6.1 Introduction 93
6.2 Key concepts and terminology 93
6.3 Constructing the decision tree for a hypothetical decision problem 96
6.4 Constructing the decision tree for a medical decision problem 103
