Medical decision making / / Harold C. Sox, Michael C. Higgins, Douglas K. Owens, Gillian Sanders Schmidler

• Autore: Sox Harold C
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• Edizione: Third edition
• Descrizione fisica: 1 online resource (365 pages)
• Disciplina: 616.07/5
• Altri autori: HigginsMichael C <1950.> (Michael Clark) OwensDouglas K Sanders SchmidlerGillian
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Dedication Page -- 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? -- CHAPTER 2 Differential diagnosis -- 2.1 An 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 Definition and key concepts -- 3.1.3 The meaning of probability: the present state vs. a future event -- 3.1.4 Odds: an alternative way to express a probability -- 3.2 How to determine a probability -- 3.2.1 Probability: a quantification of judgment about the likelihood of an event -- 3.2.2 Indirect probability assessment -- 3.2.3 Direct probability assessment -- 3.3 Sources of error in using personal experience to estimate the probability -- 3.3.1 Heuristics defined -- 3.3.2 Heuristic I: representativeness -- 3.3.3 Heuristic II: availability -- 3.3.4 Heuristic III: anchoring and adjustment -- 3.3.5 Correctly using heuristics for estimating probability -- 3.4 The role of empirical evidence in quantifying uncertainty. ; 3.4.1 Determining probability from the prevalence of disease in patients with a symptom, physical finding, or test result -- 3.4.2 Determining the probability of a disease from its prevalence in patients with a clinical syndrome -- 3.4.3 Establishing a probability using a clinical prediction model -- 3.5 Limitations of published studies of disease prevalence -- 3.5.1 Caution in using published reports to determine probability -- 3.6 Taking the special characteristics of the patient into account when determining probabilities -- Bibliography -- CHAPTER 4 Interpreting 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 The derivation of the odds ratio form of Bayes' theorem -- 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 using Bayes' theorem -- 4.5.1 Further thoughts -- 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 -- Bibliography -- CHAPTER 5 Measuring the accuracy of clinical findings -- 5.1 A language for describing test results -- 5.1.1 Defining a test result -- 5.2 The measurement of diagnostic test performance -- 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 -- 5.2.4 Predictive value -- 5.3 How to measure diagnostic test performance: a hypothetical example. ; 5.3.1 Description of the study -- 5.3.2 Description of results -- 5.3.3 An important limitation of the spleen scan study -- 5.4 Pitfalls of predictive value -- 5.5 How to perform a high quality study of diagnostic test performance -- 5.5.1 The features of a high-quality prospective study of a diagnostic test -- 5.5.2 Study characteristics that help ensure that the results apply to usual practice -- 5.5.3 Study characteristics that insure unbiased, reproducible interpretation of the index test and the gold standard test -- 5.6 Spectrum bias in the measurement of test performance -- 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: reluctance to order the gold standard test because of over-confidence in a negative index test result -- 5.6.3 Effects of spectrum bias -- 5.6.4 Adjusting for biased estimates of sensitivity and specificity -- 5.6.5 Heuristics for adjusting published reports for disease severity bias -- 5.7 When to be concerned about inaccurate measures of test performance -- 5.8 Test results as a continuous variable: the ROC curve -- 5.8.1 The distribution of test results in diseased and well individuals -- 5.8.2 The receiver operating characteristic curve -- 5.8.3 Using the ROC curve to compare tests -- 5.8.4 Setting the cut point for a test -- 5.9 Combining data from studies of test performance: the systematic review and meta-analysis -- A.5.1 Appendix: derivation of the method for using an ROC curve to choose the definition of an abnormal test result -- Bibliography -- CHAPTER 6 Decision trees - representing the structure of a decision problem -- 6.1 Introduction -- 6.2 Key concepts and terminology -- 6.2.1 Final outcomes -- 6.2.2 Branch probabilities and outcome probabilities -- 6.2.3 Expected value calculations and life expectancy. ; 6.3 Constructing the decision tree for a hypothetical decision problem -- 6.4 Constructing the decision tree for a medical decision problem -- 6.4.1 Management of coronary artery disease overview -- 6.4.2 Simple decision in the management of coronary artery disease -- 6.4.3 Determining the branch probabilities -- 6.4.4 Alternate chance node ordering -- 6.4.5 Computing the life expectancy for the decision alternatives -- Epilogue -- Bibliography -- CHAPTER 7 Decision tree analysis -- 7.1 Introduction -- 7.2 Folding-back operation -- 7.2.1 Folding-back operation applied to hypothetical problem -- 7.2.2 Chance node ordering revisited -- 7.2.3 Two-stage decision in the management of coronary artery disease -- 7.2.4 Decision tree for two-stage coronary artery disease management decision -- 7.2.5 Folding-back operation applied to two-stage coronary artery disease decision problem -- 7.2.6 Conclusion of the folding-back operation -- 7.2.7 Comment on number of significant figures used in calculations -- 7.3 Sensitivity analysis -- 7.3.1 One-way sensitivity analysis for simple decision problems -- 7.3.2 Two-way sensitivity analysis for simple decision problems -- 7.3.3 Sensitivity analysis for problems with two decisions -- 7.3.4 Sensitivity analysis and clinical policies -- Epilogue -- Bibliography -- CHAPTER 8 Outcome utility - representing risk attitudes -- 8.1 Introduction -- 8.2 What are risk attitudes? -- 8.2.1 Risk-tolerant preferences -- 8.3 Demonstration of risk attitudes in a medical context -- 8.3.1 Depicting choice of lung cancer treatment as a decision tree -- 8.3.2 Branch probabilities for the lung cancer treatment decision -- 8.3.3 von Neumann-Morgenstern utility and the outcome values -- 8.3.4 Using standard gamble assessment questions to determine outcome utilities. ; 8.3.5 Determining the outcome utilities for the lung cancer decision problem -- 8.3.6 Computing Patient A's expected utility for each of the treatments -- 8.3.7 Risk attitudes matter -- 8.4 General observations about outcome utilities -- 8.4.1 Certainty equivalent - providing a tangible meaning for expected utility analysis -- 8.4.2 Risk attitudes revisited -- 8.5 Determining outcome utilities - underlying concepts -- 8.5.1 Lifetime-tradeoff assessment -- 8.5.2 Survival-tradeoff assessment -- Epilogue -- Bibliography -- CHAPTER 9 Outcome utilities - clinical applications -- 9.1 Introduction -- 9.2 A parametric model for outcome utilities -- 9.2.1 What is a parametric model? -- 9.2.2 The exponential utility model -- 9.2.3 Scaling exponential utility models -- 9.2.4 Assumption underlying the exponential utility model -- 9.2.5 Determining the exponential utility model parameter - first approach -- 9.2.6 Determining the exponential utility model parameter - alternate assessment approach -- 9.2.7 Exponential utility model parameter and risk attitudes -- 9.3 Incorporating risk attitudes into clinical policies -- 9.3.1 Risk-adjusted clinical policies - underlying concept -- 9.3.2 Clinical context for illustrating risk-adjusted clinical policy design -- 9.3.3 Determining the risk parameter threshold -- 9.3.4 A simpler assessment question -- 9.3.5 Generalized age- and gender-specific clinical policy -- 9.3.6 Risk-adjusted clinical policies - what does it all mean? -- 9.4 Helping patients communicate their preferences -- Epilogue -- A.9.1 Exponential utility model parameter nomogram -- Bibliography -- CHAPTER 10 Outcome utilities - adjusting for the quality of life -- 10.1 Introduction -- 10.2 Example - why the quality of life matters -- 10.3 Quality-lifetime tradeoff models -- 10.3.1 Parameterizing the quality-lifetime tradeoff model. ; 10.3.2 Quality-lifetime parametric utility model with constant risk attitudes.
• Titolo autorizzato: Medical decision making
• ISBN: 1-119-62772-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910876977103321
• Lo trovi qui: Univ. Federico II