Cover -- Title Page -- Copyright -- Contents -- Foreword -- Preface -- Chapter 1 Introduction -- 1.1 How may I be thorough yet efficient when considering the possible causes of my patient's problems? -- 1.2 How do I characterize the information I have gathered during the medical interview and physical examination? -- 1.3 How do I interpret new diagnostic information? -- 1.4 How do I select the appropriate diagnostic test? -- 1.5 How do I choose among several risky treatment alternatives? -- 1.6 Summary -- Chapter 2 Differential diagnosis -- 2.1 Introduction -- 2.2 How clinicians make a diagnosis -- 2.3 The principles of hypothesis-driven differential diagnosis -- 2.3.1 The first step in differential diagnosis: listening and generating hypotheses -- 2.3.2 The second step in differential diagnosis: gathering data to test hypotheses -- 2.3.3 Hypothesis testing -- 2.3.4 Selecting a course of action -- 2.4 An extended example -- 2.4.1 Clinical aphorisms -- Bibliography -- Chapter 3 Probability: quantifying uncertainty -- 3.1 Uncertainty and probability in medicine -- 3.1.1 The uncertain nature of clinical information -- 3.1.2 Probability: a language for expressing uncertainty -- 3.1.3 Probability: a means for interpreting uncertain information -- 3.1.4 When to estimate probability -- 3.1.5 Objective and subjective probability -- 3.2 Using personal experience to estimate probability -- 3.2.1 Direct probability assessment -- 3.2.2 Indirect probability assessment -- 3.2.3 Sources of error in using personal experience to estimate probability -- 3.3 Using published experience to estimate probability -- 3.3.1 Estimating probability from the prevalence of disease in patients with a symptom, physical finding, or test result -- 3.3.2 Estimating the probability of a disease from its prevalence in patients with a clinical syndrome.

3.3.3 Clinical prediction rules for estimating probability -- 3.3.4 Limitations of published studies -- 3.4 Taking the special characteristics of the patient into account when estimating probability -- Problems -- Bibliography -- Chapter 4 Understanding new information: Bayes' theorem -- 4.1 Introduction -- 4.2 Conditional probability defined -- 4.3 Bayes' theorem -- 4.3.1 Derivation of Bayes' theorem -- 4.3.2 Clinically useful forms of Bayes' theorem -- 4.4 The odds ratio form of Bayes' theorem -- 4.4.1 Derivation -- 4.4.2 The likelihood ratio: a measure of test discrimination -- 4.4.3 Using the odds ratio form of Bayes' theorem -- 4.5 Lessons to be learned from Bayes' theorem -- 4.5.1 Insights about interpreting diagnostic tests -- 4.5.2 The clinical significance of test specificity -- 4.5.3 The clinical significance of test sensitivity -- 4.6 The assumptions of Bayes' theorem -- 4.7 Using Bayes' theorem to interpret a sequence of tests -- 4.8 Using Bayes' theorem when many diseases are under consideration -- Problems -- Bibliography -- Chapter 5 Measuring the accuracy of diagnostic information -- 5.1 How to describe test results: abnormal and normal, positive and negative -- 5.2 Measuring a test's capability to reveal the patient's true state -- 5.2.1 How to measure test performance -- 5.2.2 Measures of concordance between index test and disease state -- 5.2.3 Measures of discordance between index test and disease state -- 5.3 How to measure the characteristics of a diagnostic test: a hypothetical case -- 5.3.1 Description of study -- 5.4 Pitfalls of predictive value -- 5.5 Sources of biased estimates of test performance and how to avoid them -- 5.5.1 Study characteristics that help to insure that the results apply to usual practice.

5.5.2 Study characteristics that insure unbiased, reproducible interpretation of the index test and the gold standard test -- 5.6 Spectrum bias -- 5.6.1 The first phase of test evaluation: testing the ''sickest of the sick'' and the ''wellest of the well'' -- 5.6.2 The second phase of test evaluation: testing patients who have been referred for the gold standard test -- 5.6.3 Effects of spectrum bias -- 5.6.4 Effect of spectrum bias on the false-positive rate of a test -- 5.6.5 How to adjust for biased estimates of sensitivity and specificity -- 5.7 Expressing test results as continuous variables -- 5.7.1 The distribution of test results in diseased and well individuals -- 5.7.2 The receiver operating characteristic curve -- 5.7.3 Using the ROC curve to compare tests -- 5.7.4 Setting the cut-off value for a test -- 5.8 Combining data from several studies of test performance -- Problems -- Appendix: Derivation of the method for using an ROC curve to choose the definition of an abnormal test result -- Bibliography -- Chapter 6 Expected value decision making -- 6.1 An example -- 6.2 Selecting the decision maker -- 6.3 Decision trees: structured representations for decision~problems -- 6.4 Quantifying uncertainty -- 6.5 Probabilistic analysis of decision trees -- 6.6 Expected value calculations -- 6.7 Sensitivity analysis -- 6.8 Folding back decision trees -- Problems -- Bibliography -- Chapter 7 Markov models and time-varying outcomes -- 7.1 Markov model basics -- 7.1.1 Markov Independence -- 7.1.2 Estimating transition probabilities -- 7.1.3 Age-adjusted survival probabilities -- 7.1.4 Determining life expectancy for time-invariant acyclic Markov models -- 7.1.5 Determining life expectancy by Monte Carlo simulation -- 7.2 Exponential survival model and life expectancy -- Problems -- Appendix: Mathematical details -- Bibliography.

Chapter 8 Measuring the outcome of care-expected utility analysis -- 8.1 Basic concept-direct utility assessment -- 8.2 Sensitivity analysis-testing the robustness of utility analysis -- 8.3 Shortcut-using a linear scale to express strength of preference -- 8.4 Exponential utility-a parametric model -- 8.4.1 Exponential utility assessment -- 8.4.2 Assumption underlying the exponential utility model -- 8.4.3 Exponential utility and risk attitudes -- 8.5 Exponential utility with exponential survival -- 8.6 Multidimensional outcomes-direct assessment -- 8.7 Multidimensional outcomes-simplifications -- 8.7.1 Simplification: assume independence between preferences for length and quality of life -- 8.7.2 Simplification: assume the delta property -- 8.8 Multidimensional outcomes-quality-adjusted life years (QALY) -- 8.9 Comparison of the two models for outcomes with different length and quality -- Problems -- Appendix: Mathematical details -- Bibliography -- Chapter 9 Selection and interpretation of diagnostic tests -- 9.1 Taking action when the consequences are uncertain: principles and definitions -- 9.1.1 Three principles of decision making -- 9.1.2 The meaning of the treatment-threshold probability -- 9.2 The treatment-threshold probability -- 9.3 The decision to obtain a diagnostic test -- 9.3.1 The criteria for diagnostic testing -- 9.3.2 A method for deciding to perform a diagnostic test -- 9.3.3 The threshold probabilities for testing -- 9.4 Choosing between diagnostic tests -- 9.5 Choosing the best combination of diagnostic tests -- 9.5.1 Principles for choosing a combination of diagnostic tests -- 9.5.2 Using a computer to choose the best decision option -- 9.6 Setting the treatment-threshold probability -- 9.6.1 Estimate p* subjectively -- 9.6.2 Subjectively estimate the ratio of harms to benefits (H/B).

9.6.3 Use life expectancy to calculate p* -- 9.6.4 Use the patient's utilities for the disease-treatment states to estimate treatment harms and benefits -- 9.7 Taking account of the utility of experiencing a test -- 9.8 A clinical case: test selection for suspected brain tumor -- 9.9 Sensitivity analysis -- Bibliography -- Chapter 10 Cost-effectiveness analysis and cost-benefit analysis -- 10.1 The clinician's conflicting roles: patient advocate, member of society, and entrepreneur -- 10.1.1 The clinician as advocate for the patient -- 10.1.2 Principles for allocating scarce resources -- 10.2 Cost-effectiveness analysis: a method for comparing management strategies -- 10.2.1 Using cost-effectiveness analysis to set institutional policy -- 10.2.2 Flat-of-the-curve medicine -- 10.3 Cost-benefit analysis: a method for measuring the net benefit of medical services -- 10.3.1 The distinction between cost-benefit analysis and cost-effectiveness analysis -- 10.3.2 Placing a monetary value on human life -- 10.3.3 Should clinicians take an interest in cost-benefit analysis? -- 10.4 Measuring the costs of medical care -- 10.4.1 The direct costs of care -- 10.4.2 Productivity costs -- 10.4.3 Discounting future costs -- Problems -- Bibliography -- Chapter 11 Medical decision analysis in practice: advanced methods -- 11.1 An overview of advanced modeling techniques -- 11.1.1 When are advanced modeling approaches needed? -- 11.1.2 Types of modeling approaches -- 11.1.3 Choosing among modeling approaches -- 11.2 Use of medical decision-making concepts to analyze a policy problem: the cost-effectiveness of screening for HIV -- 11.2.1 The policy question -- 11.2.2 Steps of the analysis -- 11.2.3 Define the problem, objectives, and perspective -- 11.2.4 Identify alternatives and choose the modeling framework.

11.2.5 Structure the problem, define chance events, represent the time sequence.