| Description |
xiii, 208 pages : illustrations ; 24 cm |
| Contents |
Contents note continued: 12.6.Disaggregating the Value of Information: Expected Value of Perfect Parameter Information and the Expected Value of Sample Information -- 12.6.1.Expected Value of Perfect Parameter Information -- 12.6.2.Expected Value of Sample Information -- 12.7.Exercise: Constructing the Net Benefit Probability Map and Calculating the Value of Perfect Information -- 12.7.1.Calculating the Expected Value of Perfect Information -- 12.8.Summary -- References -- 13.1.Introduction -- 13.2.Value of Information Analysis for Research Prioritisation -- 13.3.Value of Information Analysis for Research Design -- 13.3.1.Calculating the Expected Net Present Value of Sample Information -- 13.4.Is Decision Theory Ready to Inform Trial Design'? -- 13.4.1.Structuring a Decision Problem -- 13.4.2.Evidence Synthesis and Model Parameterisation -- 13.4.3.Computational and Statistical Challenges -- 13.4.4.Adoption by Regulatory Organisations and Reimbursement Agencies -- |
|
Contents note continued: 13.4.5.Adoption by Public Research Commissioners and Clinical Trialists -- 13.4.6.Industrial Development of Health Technologies -- 13.5.Value of Information in the Evolving Regulatory and Reimbursement Environments -- 13.6.Summary -- Appendix: General Monte Carlo Sampling Algorithm for Calculation of Population ENPVSI |
|
Contents note continued: 2.6.Finding the Evidence on Health-Related Quality of Life and Health State Preferences -- 2.6.1.Where to Look -- 2.6.2.Search Strategy, Concepts, Terms and Combinations -- 2.6.3.Search Filters, Database Limits and Clinical Queries -- 2.7.Finding Evidence on Resource Use and Costs -- 2.7.1.Where to Look -- 2.7.2.Search Strategy, Concepts, Terms and Combinations -- 2.7.3.Search Filters, Database Limits and Clinical Queries -- 2.8.Tracking and Reporting Search Activities -- 2.9.Quality Assessment Tools -- 2.10.Summary -- References -- 3.1.Introduction -- 3.2.What Is a Decision Model? -- 3.3.Key Elements of a Decision Tree -- 3.4.Costs, Benefits and Complexity -- 3.5.Exercise Building a Decision Tree -- 3.6.Summary -- References -- 4.1.Introduction -- 4.2.Sources of Uncertainty in Cost Effectiveness Models -- 4.2.1.Sampling Variation -- 4.2.2.Extrapolation -- 4.2.3.Generalisability -- 4.2.4.Model Structure -- 4.2.5.Methodological Uncertainty -- |
|
Contents note continued: 4.3.Analytic Responses to Uncertainty in CEA -- 4.3.1.One-Way Sensitivity Analysis -- 4.3.2.Multiway Sensitivity Analysis -- 4.3.3.Threshold Analysis -- 4.3.4.Analysis of Extremes -- 4.4.Probabilistic Sensitivity Analysis (PSA) -- 4.5.Outputs from Probabilistic Analysis -- 4.6.Some Problems with ICERs -- 4.7.Summary -- References -- 5.1.Introduction -- 5.2.Why Use Markov Models? -- 5.3.Health States -- 5.4.Transition Probabilities -- 5.5.Markov Trace -- 5.6.Cycle Length, Time Horizon and Discounting -- 5.7.Summary -- References -- 6.1.Introduction -- 6.2.What Do We Mean by Effectiveness Parameters? -- 6.2.1.Obtaining Information on Effectiveness -- 6.3.Choosing Distributions for Effectiveness Parameters -- 6.3.1.Fitting a Distribution -- 6.4.Beta Distribution for Probabilities -- 6.5.Dirichlet Distribution for Multinomial Probabilities -- 6.6.Normal Distribution for Log-Relative Risk -- 6.7.Survival Analysis for Time-to-Event Data -- |
|
Contents note continued: 6.7.1.The Exponential Distribution -- 6.7.2.The Weibull Distribution -- 6.7.3.The Gompertz Distribution -- 6.7.4.Choice of Distribution for Time-to-Event Data -- 6.8.Parameter Correlation in Survival Analysis -- 6.9.Summary -- References -- 7.1.Introduction -- 7.2.Distributions for Cost Parameters -- 7.2.1.The LogNormal Distribution -- 7.2.2.The Gamma Distribution -- 7.3.Distributions for Utility Parameters -- 7.3.1.Distributional Characteristics of the Utility Scale -- 7.4.Characterising Uncertainty for Expected Utility Values Close to 1.0 -- 7.4.1.Characterising Uncertainty for Expected Utility Values Away from 1.0 -- 7.4.2.Logical Ordering for Utilities in Cost Effectiveness Models -- 7.4.3.Health State-Specific Side Effect Utility Decrements -- 7.5.Summary -- References -- 8.1.Introduction -- 8.2.Correlated Parameters -- 8.3.Defining a Set of Correlated Parameters -- 8.4.The Cholesky Decomposition -- |
|
Contents note continued: 8.5.What If I Need a Cholesky Decomposition for a Different Number of Variables? -- 8.6.Interpreting the Cholesky Decomposition -- 8.7.Summary -- Appendix: Extending the Cholesky Decomposition for More Than Three Correlated Parameters -- 9.1.Introduction -- 9.2.The Model -- 9.3.Modelling in Excel -- 9.4.Constructing the Parameter Table -- 9.5.Programming Your Model -- 9.6.Adding a Discount Rate, Costs and Utilities -- 9.7.Adding the Calculation of the Deterministic Incremental Cost Effectiveness Ratio (ICER) -- 9.8.Summary -- 10.1.Introduction -- 10.2.Deterministic and Probabilistic Cost Effectiveness Analysis -- 10.3.Making Model Parameters Stochastic -- 10.4.Obtaining a Probabilistic Sensitivity Analysis from a Stochastic Model -- 10.5.Exercise: Probabilistic Effectiveness Parameters -- 10.6.Exercise: Probabilistic Cost and Utility Parameters -- 10.7.Exercise: Incorporating the Cholesky Decomposition -- 10.8.Summary -- |
|
Contents note continued: Appendix: Optimising Visual Basic Macros in Excel -- 11.1.Introduction -- 11.2.Scatter Plots on the Cost Effectiveness Plane -- 11.3.Cost Effectiveness Acceptability Curves (CEACs) -- 11.4.Cost Effectiveness Acceptability Frontiers (CEAFs) -- 11.5.Scatter Plots, CEACs and CEAF Exercises -- 11.6.Summary -- References -- 12.1.Introduction -- 12.2.Uncertainty and Health-Care Reimbursement Decision-Making Processes -- 12.3.Investing in Innovative Health Technologies -- 12.4.Net Benefit Probability Map and Managing Decision Uncertainty -- 12.5.Delaying a Reimbursement Decision for More Research -- 12.5.1.Uncertainty in Decision Making and the Cost of Making the Wrong Decision -- 12.5.2.Expected Value of Perfect Information and the Value of Sample Information -- 12.5.3.Calculating the Expected Value of Perfect Information -- |
|
Machine generated contents note: 1.1.Introduction -- 1.2.Scarcity, Choice and Opportunity Cost -- 1.3.Types of Economic Evaluation -- 1.3.1.Cost Benefit Analysis (CBA) -- 1.3.2.Cost Effectiveness Analysis (CEA) -- 1.3.3.Cost Utility Analysis (CUA) -- 1.4.Incremental Cost Effectiveness Ratios (ICERs) -- 1.4.1.Simple and Extended Dominance -- 1.4.2.The Net Benefit Approach -- 1.5.Summary -- References -- 2.1.Introduction -- 2.2.Choosing Resources to Search for Evidence -- 2.3.Designing Search Strategies -- 2.4.Searching for Existing Cost Effectiveness Models -- 2.4.1.Where to Look -- 2.4.2.Search Strategy, Concepts, Terms and Combinations -- 2.4.3.Search Filters, Database Limits and Clinical Queries -- 2.5.Searching for Clinical Evidence -- 2.5.1.Finding the Evidence on Incidence, Prevalence and Natural History of a Disease -- 2.5.2.Finding the Evidence on the Clinical Effectiveness of Health Interventions -- 2.5.3.Database Limits and Clinical Queries -- |
| Summary |
This book provides an introduction to decision analytic cost-effectiveness modelling, giving the theoretical and practical knowledge required to design and implement analyses that meet the methodological standards of health technology assessment organisations. The book guides you through building a decision tree and Markov model and, importantly, shows how the results of cost-effectiveness analyses are interpreted |
| Bibliography |
Includes bibliographical references |
| Subject |
Medical economics.
|
|
Medical innovations -- Cost effectiveness -- Mathematical models.
|
|
Technology Assessment, Biomedical.
|
|
Cost-Benefit Analysis.
|
|
Medical Informatics -- economics.
|
|
Models, Theoretical.
|
| Author |
Hall, Peter A., 1950- author
|
|
Hulme, Claire, author
|
|
McCabe, Chris (Christopher J.), 1967- author
|
|
Wright, Judy (Information specialist), author
|
| ISBN |
3319157434 |
|
9783319157436 |
|
(online) |
|
