Descript 
xiii, 208 pages : illustrations (some color) ; 24 cm 

text txt rdacontent 

unmediated n rdamedia 

volume nc rdacarrier 
Note 
"Softcover reprint of the hardcover 1st edition 2015"Title page verso 

Includes bibliographical references 

"This book provides an introduction to decision analytic costeffectiveness 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 costeffectiveness analyses are interpreted. Given the complex nature of costeffectiveness modelling and the often unfamiliar language that runs alongside it, we wanted to make this book as accessible as possible whilst still providing a comprehensive, indepth, practical guide that reflects the state of the art that includes the most recent developments in costeffectiveness modelling. Although the nature of cost effectiveness modelling means that some parts are inevitably quite technical, across the 13 chapters we have broken down explanations of theory and methods into bitesized pieces that you can work through at your own pace; we have provided explanations of terms and methods as we use them. Importantly, the exercises and online workbooks allow you to test your skills and understanding as you go along. Dr Richard Edlin, PhD is a Senior Lecturer based within the Health Systems section of the School of Population Health, University of Auckland, New Zealand. Richard has published within both economics and clinicallyfocused journals, including the top field journals in health economics. Much of his research involves costeffectiveness analysis. Richard leads teaching on postgraduate cost effectiveness. Professor Christopher McCabe, PhD, holds a Capital Health Endowed Research Chair at the University of Alberta, having previously held Chairs at the Universities of Leeds, Warwick and Sheffield. He is on the health economics working group for Canadian Agency for Drugs and Technologies in Health (CADTH). He has acted as a consultant for public and private sector organizations in Europe, North America and Australasia; most notably with NICE in the UK. His primary research interest is in the development of efficient research and development processes for biotherapies and devices in the context of value based reimbursement market access hurdles. Professor Claire Hulme, PhD, holds a Chair in Health Economics and is head of the Academic Unit of Health Economics at the University of Leeds. She is on the National Institute of Health Research Health Technology Assessment Commissioning Panel in the UK. Her research interests lie in the economic evaluation of community programmes spanning the health and social care sectors, particularly economic evaluation alongside clinical trials. Dr Peter Hall, MBChB, PhD, is a Senior Clinical Lecturer at the University of Edinburgh and a visiting Health Economist at the University of Leeds. He practices as a medical oncologist with an interest in breast cancer. His research interests include the use routine healthcare data, clinical pathway analysis and Bayesian decision modelling to inform efficient research design. He has an interest in the economic evaluation of diagnostic tests and personalised medicine strategies. He is a past National Institute of Health and Clinical Excellence Scholar. Judy Wright, MSc is a Senior Information Specialist and a qualified Librarian. Within her current role she leads the development of health research information support with a team of Information Specialists located within the Academic Unit of Health Economics, University of Leeds. Judy manages a portfolio of activities supporting health economics research that includes custommade literature searching, reference management and search methodology advice."Back cover 

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 

Note continued: 2.6. Finding the Evidence on HealthRelated 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 

Note continued: 4.3. Analytic Responses to Uncertainty in CEA  4.3.1. OneWay 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 LogRelative Risk  6.7. Survival Analysis for TimetoEvent Data 

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 TimetoEvent 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 StateSpecific 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 

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 

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 HealthCare Reimbursement DecisionMaking 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 

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 

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 
Subject 
Medical innovations  Cost effectiveness  Mathematical models


Medical economics


Technology Assessment, Biomedical


CostBenefit Analysis


Medical Informatics  economics


Models, Theoretical


Medical economics. fast (OCoLC)fst01014004


Gesundheitswesen gnd


Technik gnd


KostenWirksamkeitsAnalyse gnd

Alt Author 
McCabe, Chris (Christopher J.), 1967 author


Hulme, Claire, author


Hall, Peter A., 1950 author


Wright, Judy (Information specialist), author

