Cost effectiveness modelling for health technology assessment: a practical course
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Cham [u.a.]
Adis
2015
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 208 S. graph. Darst. |
| ISBN: | 9783319157436 |
Internformat
MARC
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Datensatz im Suchindex
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Titel: Cost effectiveness modelling for health technology assessment
Autor: Edlyn, Richard
Jahr: 2015
Contents
1 Economic Evaluation, Cost Effectiveness Analysis
and Health Care Resource Allocation. 1
1.1 Introduction. 1
1.2 Scarcity, Choice and Opportunity Cost. 2
1.3 Types of Economic Evaluation. 2
1.3.1 Cost Benefit Analysis (CBA). 3
1.3.2 Cost Effectiveness Analysis (CEA). 3
1.3.3 Cost Utility Analysis (CUA). 5
1.4 Incremental Cost Effectiveness Ratios (ICERs). 6
1.4.1 Simple and Extended Dominance. 9
1.4.2 The Net Benefit Approach. 11
1.5 Summary. 12
References. 13
2 Finding the Evidence for Decision Analytic
Cost Effectiveness Models. 15
2.1 Introduction. 15
2.2 Choosing Resources to Search for Evidence. 17
2.3 Designing Search Strategies. 18
2.4 Searching for Existing Cost Effectiveness Models. 19
2.4.1 Where to Look. 20
2.4.2 Search Strategy, Concepts, Terms and Combinations. 21
2.4.3 Search Filters, Database Limits and Clinical Queries. 22
2.5 Searching for Clinical Evidence. 23
2.5.1 Finding the Evidence on Incidence, Prevalence
and Natural History of a Disease. 23
2.5.2 Finding the Evidence on the Clinical Effectiveness
of Health Interventions. 24
2.5.3 Database Limits and Clinical Queries. 28
Contents
2.6 Finding the Evidence on Health-Related Quality
of Life and Health State Preferences. 30
2.6.1 Wherc to Look. 30
2.6.2 Search Strategy, Concepts, Terms and Combinations. 31
2.6.3 Search Filters, Database Limits and Clinical Queries. 32
2.7 Finding Evidence on Resource Use and Costs. 32
2.7.1 Wherc to Look. 34
2.7.2 Search Strategy, Concepts, Terms and Combinations. 34
2.7.3 Search Filters, Database Limits and Clinical Queries. 36
2.8 Tracking and Reporting Search Activities. 36
2.9 Quality Assessment Tools. 37
2.10 Summary. 38
References. 38
3 Building a Decision Tree Cost Effectiveness Model. 41
3.1 Introduction. 41
3.2 What Is a Decision Model?. 42
3.3 Key Elements of a Decision Tree. 44
3.4 Costs, Bencfits and Complexity. 49
3.5 Exercise Building a Decision Tree. 51
3.6 Summary. 56
References. 57
4 Uncertainty, Probabilistic Analysis and Outputs from
Cost Effectiveness Analyses. 59
4.1 Introduction. 59
4.2 Sources of Uncertainty in Cost Effectiveness Models. 60
4.2.1 Sampling Variation. 60
4.2.2 Extrapolation. 61
4.2.3 Generalisability. 62
4.2.4 Model Structure. 62
4.2.5 Methodological Uncertainty. 63
4.3 Analytic Responses to Uncertainty in CEA. 64
4.3.1 One-Way Sensitivity Analysis. 65
4.3.2 Multiway Sensitivity Analysis. 66
4.3.3 Threshold Analysis. 67
4.3.4 Analysis of Extremes. 67
4.4 Probabilistic Sensitivity Analysis (PSA). 68
4.5 Outputs from Probabilistic Analysis. 71
4.6 Some Problems with ICERs. 72
4.7 Summary. 75
References. 76
5 Introduction to Markov Cost Effectiveness Models. 77
5.1 Introduction. 77
5.2 Why Use Markov Models?. 78
5.3 Health States. 79
Contents xi
5.4 Transition Probabilities. 80
5.5 Markov Trace. 81
5.6 Cycle Length, Time Horizon and Discounting. 83
5.7 Summary. 86
References. 86
6 Probability Distributions for Effectiveness Parameters. 87
6.1 Introduction. 87
6.2 What Do We Mean by Effectiveness Parameters?. 88
6.2.1 Obtaining Information on Effectiveness. 88
6.3 Choosing Distributions for Effectiveness Parameters. 89
6.3.1 Fitting a Distribution. 90
6.4 Beta Distribution for Probabilities. 91
6.5 Dirichlet Distribution for Multinomial Probabilities. 94
6.6 Normal Distribution for Log-Relative Risk. 95
6.7 Survival Analysis for Time-to-Event Data. 96
6.7.1 The Exponential Distribution. 97
6.7.2 The Weibull Distribution. 98
6.7.3 The Gompertz Distribution. 99
6.7.4 Choice of Distribution for Time-to-Event Data. 100
6.8 Parameter Correlation in Survival Analysis. 102
6.9 Summary. 103
References. 103
7 Probability Distributions for Cost and Utility Parameters. 105
7.1 Introduction. 105
7.2 Distributions for Cost Parameters. 106
7.2.1 The LogNormal Distribution. 106
7.2.2 The Gamma Distribution. 108
7.3 Distributions for Utility Parameters. 109
7.3.1 Distributional Characteristics of the Utility Scale. 111
7.4 Characterising Uncertainty for Expected Utility
Values Close to 1.0. 112
7.4.1 Characterising Uncertainty for Expected Utility
Values Away from 1.0. 112
7.4.2 Logical Ordering for Utilities in Cost Effectiveness
Models. 114
7.4.3 Health State-Specific Side Effect Utility Decrements. 116
7.5 Summary. 117
References. 117
8 Correlated Parameters and the Cholesky Decomposition. 119
8.1 Introduction. 119
8.2 Correlated Parameters. 120
8.3 Defining a Set of Correlated Parameters. 121
8.4 The Cholesky Decomposition. 125
Contents xiii
12.4 Net Benefit Probability Map and Managing
Decision Uncertainty. 181
12.5 Delaying a Reimbursement Decision for More Research. 185
12.5.1 Uncertainty in Decision Making and the Cost
of Making the Wrong Decision. 186
12.5.2 Expected Value of Perfect Information
and the Value of Sample Information. 187
12.5.3 Calculating the Expected Value of Perfect Information. 190
12.6 Disaggregating the Value of Information:
Expected Value of Perfect Parameter Information
and the Expected Value of Sample Information. 191
12.6.1 Expected Value of Perfect Parameter Information. 191
12.6.2 Expected Value of Sample Information. 192
12.7 Exercise: Constructing the Net Benefit Probability
Map and Calculating the Value of Perfect Information. 192
12.7.1 Calculating the Expected Value of Perfect Information. 193
12.8 Summary. 196
References. 197
13 Value of Information in Health Technology Regulation
and Reimbursement. 199
13.1 Introduction. 199
13.2 Value of Information Analysis for Research Prioritisation. 200
13.3 Value of Information Analysis for Research Design. 200
13.3.1 Calculating the Expected Net Present Value
of Sample Information. 201
13.4 Is Decision Theory Ready to Inform Trial Design?. 203
13.4.1 Structuring a Decision Problem. 204
13.4.2 Evidence Synthesis and Model Parameterisation. 204
13.4.3 Computational and Statistical Challenges. 204
13.4.4 Adoption by Regulatory Organisations
and Reimbursement Agencies. 204
13.4.5 Adoption by Public Research Commissioners
and Clinical Trialists. 205
13.4.6 Industrial Development of Health Technologies. 205
13.5 Value of Information in the Evolving Regulatory
and Reimbursement Environments. 206
13.6 Summary. 206
Appendix. 207
Appendix 13.1: General Monte Carlo Sampling Algorithm
for Calculation of Population ENPVSI. 207
References. 208 |
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| physical | XIII, 208 S. graph. Darst. |
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| title | Cost effectiveness modelling for health technology assessment a practical course |
| title_auth | Cost effectiveness modelling for health technology assessment a practical course |
| title_exact_search | Cost effectiveness modelling for health technology assessment a practical course |
| title_full | Cost effectiveness modelling for health technology assessment a practical course Richard Edlin ... |
| title_fullStr | Cost effectiveness modelling for health technology assessment a practical course Richard Edlin ... |
| title_full_unstemmed | Cost effectiveness modelling for health technology assessment a practical course Richard Edlin ... |
| title_short | Cost effectiveness modelling for health technology assessment |
| title_sort | cost effectiveness modelling for health technology assessment a practical course |
| title_sub | a practical course |
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