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020 _a9781119627845
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020 _a1119627842
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020 _a9781119627876
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035 _a(OCoLC)1389488752
040 _aDLC
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_223/eng/20230703
100 1 _aSox, Harold C.,
_0https://id.loc.gov/authorities/names/n86038233
_eauthor.
245 1 0 _aMedical decision making /
_cHarold C. Sox, Michael C. Higgins, Douglas K. Owens, Gillian Sanders Schmidler.
250 _aThird edition.
264 1 _aHoboken, NJ :
_bJohn Wiley & Sons, Inc.,
_c2024.
264 4 _c©2024.
300 _a1 online resource (xiv, 348 pages) :
_billustrations.
336 _atext
_btxt
_2rdacontent.
337 _acomputer
_bc
_2rdamedia.
338 _aonline resource
_bcr
_2rdacarrier.
340 _2rdacc
_0http://rdaregistry.info/termList/RDAColourContent/1003.
500 _aIncludes bibliographical references and index.
505 0 _aTable of Contents 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 Epilogue 112 Bibliography 112 7 Decision tree analysis 113 7.1 Introduction 113 7.2 Folding- back operation 114 7.3 Sensitivity analysis 126 Epilogue 133 Bibliography 133 8 Outcome utility – representing risk attitudes 134 8.1 Introduction 134 8.2 What are risk attitudes? 135 8.3 Demonstration of risk attitudes in a medical context 136 8.4 General observations about outcome utilities 147 8.5 Determining outcome utilities – underlying concepts 151 Epilogue 157 Bibliography 158 9 Outcome utilities – clinical applications 159 9.1 Introduction 159 9.2 A parametric model for outcome utilities 160 9.3 Incorporating risk attitudes into clinical policies 172 9.4 Helping patients communicate their preferences 181 Epilogue 185 A.9.1 Exponential utility model parameter nomogram 186 Bibliography 188 10 Outcome utilities – adjusting for the quality of life 189 10.1 Introduction 189 10.2 Example – why the quality of life matters 190 10.3 Quality- lifetime tradeoff models 193 10.4 Quality- survival tradeoff models 203 10.5 What does it all mean? – an extended example 209 Epilogue 217 Bibliography 217 11 Survival models: representing uncertainty about the length of life 218 11.1 Introduction 218 11.2 Survival model basics 219 11.3 Medical example – survival after breast cancer recurrence 226 11.4 Exponential survival model 228 11.5 Actuarial survival models 232 11.6 Two- part survival models 235 Epilogue 247 Bibliography 247 12 Markov models 248 12.1 Introduction 248 12.2 Markov model basics 249 12.3 Determining transition probabilities 259 12.4 Markov model analysis – an overview 269 Epilogue 277 Bibliography 277 13 Selection and interpretation of diagnostic tests 278 13.1 Introduction 278 13.2 Four principles of decision making 279 13.3 The threshold probability for treatment 281 13.4 Threshold probabilities for testing 288 13.5 Clinical application of the threshold model of decision making 293 13.6 Accounting for the non- diagnostic effects of undergoing a test 296 13.7 Sensitivity analysis 298 13.8 Decision curve analysis 300 Bibliography 302 14 Medical decision analysis in practice: advanced methods 303 14.1 An overview of advanced modeling techniques 303 14.2 Use of medical decision- making concepts to analyze a policy problem: the cost- effectiveness of screening for HIV 305 14.3 Use of medical decision- making concepts to analyze a clinical diagnostic problem: strategies to diagnose tumors in the lung 313 14.4 Calibration and validation of decision models 317 14.5 Use of complex models for individual- patient decision making 319 Bibliography 321 15 Cost- effectiveness analysis 323 15.1 The clinician’s conflicting roles: patient advocate member of society and entrepreneur 323 15.2 Cost- effectiveness analysis: a method for comparing management strategies 325 15.3 Cost–benefit analysis: a method for measuring the net benefit of medical services 330 15.4 Methodological best practices for cost- effectiveness analysis 332 15.5 Reference case for cost- effectiveness analysis 333 15.6 Impact inventory for cataloguing consequences 334 15.7 Measuring the health effects of medical care 334 15.8 Measuring the costs of medical care 335 15.9 Interpretation of cost- effectiveness analysis and use in decision making 337 15.10 Limitations of cost- effectiveness analyses 337 Bibliography 338 Index 340
520 _a"This volume addresses these needs. Chapters 1 and 2 set the stage: uncertainty is everywhere in clinical practice, yet clinical reasoning depends on logical deduction as exemplified by differential diagnosis. Chapters 3, 4, and 5 are about defining and navigating uncertainty: determining probability, updating probability, and the determinants of post-test probability, all basic tools of the clinician. Chapters 6 and 7 are about modeling the factors that shape decisions. Chapters 8-12 explore in-depth the measurement of utility, both the basics and the underlying theory. Topics include attitudes toward taking risks, the quality of life, and the length of life. The last three chapters are about making decisions: deciding when to treat, when to test, and when to wait (Chapter 13); the advanced modeling methods that inform policy. (Chapter 14); and cost-effectiveness analysis (Chapter 15)"--
_cProvided by publisher.
545 0 _aAbout the Authors 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.
650 0 _aClinical medicine
_xDecision making.
_0https://id.loc.gov/authorities/subjects/sh98001051.
650 0 _aDiagnosis
_0https://id.loc.gov/authorities/subjects/sh85037489
_xDecision making.
_0https://id.loc.gov/authorities/subjects/sh99005493.
650 0 _aDiagnosis, Differential.
_0https://id.loc.gov/authorities/subjects/sh85037493.
650 0 _aDecision making.
_0https://id.loc.gov/authorities/subjects/sh85036199.
650 0 _aDecision making
_xMathematical models.
_0https://id.loc.gov/authorities/subjects/sh85036200.
650 0 _aProbabilities.
_0https://id.loc.gov/authorities/subjects/sh85107090.
650 0 _aCost effectiveness.
_0https://id.loc.gov/authorities/subjects/sh85033197.
650 2 2 _aDecision Making.
_0https://id.nlm.nih.gov/mesh/D003657.
650 2 2 _aProbability.
_0https://id.nlm.nih.gov/mesh/D011336.
650 2 2 _aCost-Benefit Analysis.
_0https://id.nlm.nih.gov/mesh/D003362.
650 1 2 _aClinical Decision-Making.
_0https://id.nlm.nih.gov/mesh/D000066491.
650 1 2 _aDiagnosis, Differential.
_0https://id.nlm.nih.gov/mesh/D003937.
650 2 2 _aDecision Support Techniques.
_0https://id.nlm.nih.gov/mesh/D003661.
655 4 _aElectronic books.
700 1 _aHiggins, Michael C.
_q(Michael Clark),
_d1950-
_0https://id.loc.gov/authorities/names/n2012190181
_eauthor.
700 1 _aOwens, Douglas K.,
_0https://id.loc.gov/authorities/names/n2004123814
_eauthor.
700 1 _aSanders Schmidler, Gillian,
_0https://id.loc.gov/authorities/names/n2011183147
_eauthor.
856 4 0 _uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781119627876
_yFull text is available at Wiley Online Library Click here to view
942 _2ddc
_cER