Modeling in medical decision making: a Bayesian approach
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Chichester [u.a.]
Wiley
2002
|
| Series: | Statistics in practice
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XI, 266 S. graph. Darst. |
| ISBN: | 0471986089 |
Staff View
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| 650 | 4 | |a Bayesian statistical decision theory | |
| 650 | 4 | |a Decision Making, Computer-Assisted | |
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Record in the Search Index
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| adam_text | Contents
Preface ix
Part I Methods 1
1 Inference 3
1.1 Summary 3
1.2 Medical diagnosis 5
1.2.1 Sensitivity, specificity and positive predictive value ... 5
1.2.2 Bayes rule 9
1.2.3 Odds, Bayes factors and the nomogram 11
1.3 Genetic counseling 15
1.3.1 Genetic susceptibility to breast and ovarian cancer ... 15
1.3.2 Individualized probabilities 16
1.3.3 Genetic parameters ¦. 18
1.3.4 Bayes rule in genetic counseling 20
1.3.5 Conditional independence 22
1.3.6 Bayes factors for family history 23
1.3.7 Sequential use of Bayes rule 29
1.4 Estimating sensitivity and specificity 31
1.4.1 Population and statistical model 31
1.4.2 Prior and posterior distributions 33
1.4.3 Distributions of positive and negative predictive values . 37
1.4.4 Beta prior distributions 38
1.4.5 Prior data conflict 40
1.4.6 Future patients 44
1.5 Chronic disease modeling 46
1.5.1 A simple Markov process for strokes the /xSPPM ... 46
1.5.2 Estimating transition models from cohort data 49
2 Decision making 51
2.1 Summary 51
vi CONTENTS
2.2 Foundations of expected utility theory 52
2.2.1 Formalizing health interventions and their consequences 53
2.2.2 The expected utility principle 54
2.2.3 Historical background 55
2.2.4 Axiomatic foundations 57
2.3 Measuring the value of avoiding a major stroke 59
2.3.1 Elicitation of personal utilities using the standard
gamble approach 60
2.3.2 Quality adjusted life years and the time trade off method 62
2.3.3 Relationship between the standard gamble and the time
trade off method 64
2.3.4 Combining time trade off and standard gamble to elicit
utilities of risk averse patients 65
2.3.5 Using individual utilities in medical decisions 67
2.4 Decision making in health care 68
2.4.1 Evidence based medicine and synthesis of evidence ... 68
2.4.2 Elements of a decision model 71
2.4.3 Variation and uncertainty in decision modeling 73
2.4.4 Expected utilities 75
2.4.5 Plug in approximation to expected utility 77
2.5 Cost effectiveness analyses in the /jSPPM 78
2.5.1 From decision modeling to cost effectiveness 78
2.5.2 The /nSPPM example 80
2.6 Statistical decision problems 82
2.6.1 Statistical decision theory 82
2.6.2 Loss functions 83
2.6.3 Implications of the expected utility principle for
statistical reasoning 84
2.6.4 Hypothesis testing 85
2.6.5 Medical diagnosis 88
2.6.6 Choosing the number of subjects in a study 89
3 Simulation 91
3.1 Summary 91
3.2 Inference via simulation 92
3.2.1 Inference on sensitivity and specificity without a gold
standard 92
3.2.2 Sampling from the posterior distribution 95
3.2.3 Markov chain Monte Carlo methods 99
3.3 Prediction and expected utility via simulation 103
3.3.1 Generating synthetic cohorts 103
3.3.2 Generating a synthetic cohort in the /xSPPM 104
3.4 Sensitivity analysis via simulation 105
3.4.1 Probabilistic sensitivity analysis 105
CONTENTS vii
3.4.2 Probabilistic sensitivity analysis of the MCER in the
MSPPM 107
3.5 Searching for strategies via simulation Ill
3.5.1 A two phase design in screening for a rare disease .... Ill
3.5.2 Monte Carlo evaluation of the expected utility 113
3.5.3 Markov chain sampling of actions 116
Part II Case Studies 121
4 Meta analysis 123
4.1 Summary 123
4.2 Meta analysis 123
4.3 Bayesian meta analysis 126
4.4 Tamoxifen in early breast cancer 127
4.4.1 Background 127
4.4.2 Modeling heterogeneity 129
4.4.3 Computing 133
4.4.4 Results 136
4.5 Combining studies with continuous and dichotomous responses 138
4.5.1 A simulated example 139
4.6 Migraine headache 142
4.6.1 Background and goals 142
4.6.2 Data 144
4.6.3 Sampling distributions and latent variables 146
4.6.4 Treatment and study variation 149
4.6.5 Prior distributions 152
4.6.6 Computing 153
4.6.7 Results 154
4.6.8 Comparisons with alternative approaches 157
4.6.9 Graphical model checking 159
4.6.10 Sensitivity to prior specification 163
5 Decision trees 167
5.1 Summary 167
5.2 Axillary lymph node dissection in early breast cancer 167
5.2.1 Why a decision model? 169
5.3 A simple decision tree 170
5.3.1 Nodes, branches, and leaves 170
5.3.2 Backward induction 173
5.3.3 Measuring the value of prognostic information 174
5.3.4 The value of information 176
5.4 A more complete decision tree for ALND 177
5.4.1 Tree structure 177
viii CONTENTS
5.4.2 Population model 178
5.4.3 Quality of life 180
5.4.4 Solving the tree for a specific patient type 182
5.4.5 Mapping the value of ALND in the covariates space . . 185
5.4.6 Uncertainty and probabilistic sensitivity analysis .... 189
6 Chronic disease modeling 195
6.1 Summary 195
6.2 Model overview 195
6.2.1 Modeling in breast cancer screening 195
6.2.2 Model components 196
6.3 Natural history model 198
6.3.1 States and transitions 198
6.3.2 Transition densities and sojourn time distribution . . . 200
6.3.3 Estimating transition densities via deconvolution .... 202
6.3.4 Alternative estimates of preclinical sojourn time .... 204
6.3.5 Age of onset of preclinical disease 207
6.3.6 Other risk factors 210
6.4 Modeling the effects of screening 210
6.4.1 Sensitivity model 210
6.4.2 Axillary lymph node involvement model 211
6.4.3 Survival model 212
6.4.4 Costs 213
6.5 Comparing screening schedules 213
6.5.1 Screening strategies 213
6.5.2 Balance sheet 215
6.5.3 Trade off analysis 216
6.6 Model critique 219
6.6.1 Limitations of the model 219
6.6.2 Clinical trials and screening women in their forties . . . 221
6.6.3 Complementarity of trials and decision models 222
6.7 Optimizing screening schedules 225
6.7.1 Recursive approach 225
6.7.2 Variational approach 228
References 235
Index 259
|
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| dewey-ones | 610 - Medicine and health |
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| dewey-sort | 3610 17 227 |
| dewey-tens | 610 - Medicine and health |
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| format | Book |
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| indexdate | 2024-07-09T18:58:34Z |
| institution | BVB |
| isbn | 0471986089 |
| language | English |
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| physical | XI, 266 S. graph. Darst. |
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| spelling | Parmigiani, Giovanni Verfasser (DE-588)124676952 aut Modeling in medical decision making a Bayesian approach Giovanni Parmigiani Chichester [u.a.] Wiley 2002 XI, 266 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Statistics in practice Besliskunde gtt Geneeskunde gtt Methode van Bayes gtt Modellen gtt Médecine - Prise de décision - Méthodes statistiques Statistique bayésienne Medizin Bayes Theorem Bayesian statistical decision theory Decision Making, Computer-Assisted Decision Support Techniques Medicine Decision making Statistical methods Models, Statistical Medizinische Statistik (DE-588)4127563-9 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 s Medizinische Statistik (DE-588)4127563-9 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009698106&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Parmigiani, Giovanni Modeling in medical decision making a Bayesian approach Besliskunde gtt Geneeskunde gtt Methode van Bayes gtt Modellen gtt Médecine - Prise de décision - Méthodes statistiques Statistique bayésienne Medizin Bayes Theorem Bayesian statistical decision theory Decision Making, Computer-Assisted Decision Support Techniques Medicine Decision making Statistical methods Models, Statistical Medizinische Statistik (DE-588)4127563-9 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| subject_GND | (DE-588)4127563-9 (DE-588)4144220-9 |
| title | Modeling in medical decision making a Bayesian approach |
| title_auth | Modeling in medical decision making a Bayesian approach |
| title_exact_search | Modeling in medical decision making a Bayesian approach |
| title_full | Modeling in medical decision making a Bayesian approach Giovanni Parmigiani |
| title_fullStr | Modeling in medical decision making a Bayesian approach Giovanni Parmigiani |
| title_full_unstemmed | Modeling in medical decision making a Bayesian approach Giovanni Parmigiani |
| title_short | Modeling in medical decision making |
| title_sort | modeling in medical decision making a bayesian approach |
| title_sub | a Bayesian approach |
| topic | Besliskunde gtt Geneeskunde gtt Methode van Bayes gtt Modellen gtt Médecine - Prise de décision - Méthodes statistiques Statistique bayésienne Medizin Bayes Theorem Bayesian statistical decision theory Decision Making, Computer-Assisted Decision Support Techniques Medicine Decision making Statistical methods Models, Statistical Medizinische Statistik (DE-588)4127563-9 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| topic_facet | Besliskunde Geneeskunde Methode van Bayes Modellen Médecine - Prise de décision - Méthodes statistiques Statistique bayésienne Medizin Bayes Theorem Bayesian statistical decision theory Decision Making, Computer-Assisted Decision Support Techniques Medicine Decision making Statistical methods Models, Statistical Medizinische Statistik Bayes-Entscheidungstheorie |
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