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[null,"1.","Record Nr.",null,"UNINA9910300166503321"]
[null,null,"Autore",null,"Edlin Richard"]
[null,null,"Titolo",null,"Cost Effectiveness Modelling for Health Technology Assessment : A Practical Course / / by Richard Edlin, Christopher McCabe, Claire Hulme, Peter Hall, Judy Wright"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Adis, , 2015"]
[null,null,"ISBN",null,"3-319-15744-2"]
[null,null,"Edizione",null,"[1st ed. 2015.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XIII, 208 p. 86 illus., 3 illus. in color.)"]
[null,null,"Disciplina",null,"362.1068"]
[null,null,"Soggetti",null,"Pharmacy management"]
[{"$nulls":4},"Medicine - Research"]
[{"$nulls":4},"Medical informatics"]
[{"$nulls":4},"Medical economics"]
[{"$nulls":4},"Pharmacoeconomics and Health Outcomes"]
[{"$nulls":4},"Quality of Life Research"]
[{"$nulls":4},"Health Informatics"]
[{"$nulls":4},"Health Economics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Acknowledgements -- Contents -- Chapter 1: Economic Evaluation, Cost Effectiveness Analysis and Health Care Resource Allocation -- 1.1 Introduction -- 1.2 Scarcity, Choice and Opportunity Cost -- 1.3 Types of Economic Evaluation -- 1.3.1 Cost Benefit Analysis (CBA) -- 1.3.2 Cost Effectiveness Analysis (CEA) -- 1.3.3 Cost Utility Analysis (CUA) -- 1.4 Incremental Cost Effectiveness Ratios (ICERs) -- 1.4.1 Simple and Extended Dominance -- 1.4.2 The Net Benefit Approach -- 1.5 Summary -- References -- Chapter 2: Finding the Evidence for Decision Analytic Cost effectiveness Models -- 2.1 Introduction -- 2.2 Choosing Resources to Search for Evidence -- 2.3 Designing Search Strategies -- 2.4 Searching for Existing Cost effectiveness Models -- 2.4.1 Where to Look -- 2.4.2 Search Strategy, Concepts, Terms and Combinations -- 2.4.3 Search Filters, Database Limits and Clinical Queries -- 2.5 Searching for Clinical Evidence -- 2.5.1 Finding the Evidence on Incidence, Prevalence and Natural History of a Disease -- 2.5.2 Finding the Evidence on the Clinical Effectiveness of Health Interventions -- 2.5.3 Database Limits and Clinical Queries -- 2.6 Finding the Evidence "]
[{"$nulls":4},"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 -- Chapter 3: Building a Decision Tree Cost Effectiveness Model -- 3.1 Introduction -- 3.2 What Is a Decision Model? -- 3.3 Key Elements of a Decision Tree."]
[{"$nulls":4},"3.4 Costs, Benefits and Complexity -- 3.5 Exercise Building a Decision Tree -- 3.6 Summary -- References -- Chapter 4: Uncertainty, Probabilistic Analysis and Outputs from Cost Effectiveness Analyses -- 4.1 Introduction -- 4.2 Sources of Uncertainty in Cost Effectiveness Models -- 4.2.1 Sampling Variation -- 4.2.2 Extrapolation -- 4.2.3 Generalisability -- 4.2.4 Model Structure -- 4.2.5 Methodological Uncertainty -- 4.3 Analytic Responses to Uncertainty in CEA -- 4.3.1 One-Way Sensitivity Analysis -- 4.3.2 Multiway Sensitivity Analysis -- 4.3.3 Threshold Analysis -- 4.3.4 Analysis of Extremes -- 4.4 Probabilistic Sensitivity Analysis (PSA) -- 4.5 Outputs from Probabilistic Analysis -- 4.6 Some Problems with ICERs -- 4.7 Summary -- References -- Chapter 5: Introduction to Markov Cost Effectiveness Models -- 5.1 Introduction -- 5.2 Why Use Markov Models? -- 5.3 Health States -- 5.4 Transition Probabilities -- 5.5 Markov Trace -- 5.6 Cycle Length, Time Horizon and Discounting -- 5.7 Summary -- References -- Chapter 6: Probability Distributions for Effectiveness Parameters -- 6.1 Introduction -- 6.2 What Do We Mean by Effectiveness Parameters? -- 6.2.1 Obtaining Information on Effectiveness -- 6.3 Choosing Distributions for Effectiveness Parameters -- 6.3.1 Fitting a Distribution -- 6.4 Beta Distribution for Probabilities -- 6.5 Dirichlet Distribution for Multinomial Probabilities -- 6.6 Normal Distribution for Log-Relative Risk -- 6.7 Survival Analysis for Time-to-Event Data -- 6.7.1 The Exponential Distribution -- 6.7.2 The Weibull Distribution -- 6.7.3 The Gompertz Distribution -- 6.7.4 Choice of Distribution for Time-to-Event Data -- 6.8 Parameter Correlation in Survival Analysis -- 6.9 Summary -- References -- Chapter 7: Probability Distributions for Cost and Utility Parameters -- 7.1 Introduction -- 7.2 Distributions for Cost Parameters."]
[{"$nulls":4},"7.2.1 The LogNormal Distribution -- 7.2.1.1 LogNormal via Method of Moments I -- 7.2.1.2 LogNormal via Method of Moments II -- 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 -- Chapter 8: Correlated Parameters and the Cholesky Decomposition -- 8.1 Introduction -- 8.2 Correlated Parameters -- 8.3 Defining a Set of Correlated Parameters -- 8.4 The Cholesky Decomposition -- 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 -- Appendix 8.1: Extending the Cholesky Decomposition for More Than Three Correlated Parameters -- Chapter 9: Building a Markov Cost Effectiveness Model in Excel -- 9.1 Introduction -- 9.2 The Model -- 9.3 Modelling in Excel -- 9.4 Constructing the Parameter Table -- 9.5 "]
[{"$nulls":4},"Programming Your Model -- 9.6 Adding a Discount Rate, Costs and Utilities -- 9.7 Adding the Calculation of the Deterministic Incremental Cost Effectiveness Ratio (ICER) -- 9.8 Summary -- Chapter 10: Making a Markov Model Probabilistic -- 10.1 Introduction -- 10.2 Deterministic and Probabilistic Cost Effectiveness Analysis -- 10.3 Making Model Parameters Stochastic -- 10.4 Obtaining a Probabilistic Sensitivity Analysis from a Stochastic Model -- 10.5 Exercise: Probabilistic Effectiveness Parameters -- 10.6 Exercise: Probabilistic Cost and Utility Parameters -- 10.7 Exercise: Incorporating the Cholesky Decomposition -- 10.8 Summary -- Appendix."]
[{"$nulls":4},"Appendix 10.1: Optimising Visual Basic Macros in Excel -- Chapter 11: Outputs from Probabilistic Sensitivity Analysis -- 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 -- Chapter 12: Investing in Health Care, Research and the Value of Information -- 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 -- 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 -- Chapter 13: Value of Information in Health Technology Regulation and Reimbursement -- 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."]
[{"$nulls":4},"13.4.2 Evidence Synthesis and Model Parameterisation -- 13.4.3 Computational and Statistical Challenges -- 13.4.4 Adoption by Regulatory Organisations and Reimbursement Agencies -- 13.4.5 Adoption by Public Research Commissioners and Clinical Trialists -- 13.4.6 Industrial Development of Health Technologies -- 13.5 Value of Information in the Evolving Regulatory and Reimbursement Environments -- 13.6 Summary -- Appendix -- Appendix 13.1: General Monte Carlo Sampling Algorithm for Calculation of Population ENPVSI -- References."]
[null,null,null,"Sommario/riassunto"," This book provides an introduction to decision analytic cost-effectiveness modelling, giving the theoretical and practical knowledge required to design and implement analyses that meet the methodological standards of health technology assessment organisations. The book guides you through building a decision tree and Markov model and, importantly, shows how the results of cost-effectiveness analyses are interpreted.   Given the complex nature of cost-effectiveness modelling and the often unfamiliar language that runs alongside it, we wanted to make this book as accessible as possible whilst still providing a comprehensive, in-depth, practical guide that reflects the state of the art –  that includes the most recent "]
[{"$nulls":4},"developments in cost-effectiveness 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 bite-sized 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 clinically-focused journals, including the top field journals in health economics.  Much of his research involves cost-effectiveness 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 custom-made literature searching, reference management and search methodology advice.  ."]
[null,"2.","Record Nr.",null,"UNINA9910253964603321"]
[null,null,"Autore",null,"Sanchez Daniela"]
[null,null,"Titolo",null,"Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation / / by Daniela Sanchez, Patricia Melin"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016"]
[null,null,"ISBN",null,"3-319-28862-8"]
[null,null,"Edizione",null,"[1st ed. 2016.]"]
[null,null,"Descrizione fisica",null,"1 online resource (107 p.)"]
[null,null,"Collana",null,"SpringerBriefs in Computational Intelligence, , 2625-3712"]
[null,null,"Disciplina",null,"620"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Neural networks (Computer science)"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Mathematical Models of Cognitive Processes and Neural Networks"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Introduction -- Background and Theory -- Proposed Method -- Application to Human Recognition -- Experimental Results -- Conclusions."]
[null,null,null,"Sommario/riassunto","In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms."]