Chapter 1: Introduction; 1.1 Introduction; 1.2 Process of Developing the Book; 1.3 Outline of the Book; 1.4 Future Direction; References; Part I: Foundations of MCDA in Healthcare; Chapter 2: Theoretical Foundations of MCDA; 2.1 Introduction; 2.2 Principles of MCDA and Decision-Making; 2.3 Problem Structuring; 2.4 Model Building; 2.4.1 Value Measurement; 2.4.1.1 Multi-attribute Value Theory; 2.4.1.2 Multi-Attribute Utility Theory (MAUT); 2.4.1.3 Analytic Hierarchy Process; 2.4.2 Outranking; 2.4.3 Goal Programming; 2.5 Concluding Remarks; References.

3.3.5 Step 5: Scoring the Criteria to Evaluate Performance of the Intervention3.3.6 Step 6: Aggregating Data for Ranking, Investing, and Disinvesting; 3.3.7 Step 7: Dealing with Uncertainty; 3.3.8 Step 8: Reporting Results, Deliberation, Decision, Communication, and Implementation; 3.4 Conclusion; References; Chapter 4: Incorporating Preferences and Priorities into MCDA: Selecting an Appropriate Scoring and Weighting Technique; 4.1 Introduction; 4.2 Overview of Weighting and Scoring Techniques; 4.2.1 Direct Rating; 4.2.2 Keeney-Raiffa MCDA; 4.2.2.1 Construction of Partial Value Functions.

4.2.2.2 Swing Weighting4.2.3 Pairwise Comparison Using Ordinal Scales (Analytic Hierarchy Process); 4.2.4 Discrete Choice Experiment; 4.3 Which Scoring and Weighting Techniques Are Most Appropriate?; 4.3.1 'Validity' of Scores and Weights; 4.3.1.1 Do Scores Display Interval Properties?; 4.3.1.2 Do Weights Reflect Scaling Constant or Trade-Offs?; 4.3.2 Cognitive Burden on Stakeholders; 4.3.3 Interpreting the Outputs from MCDA; 4.3.4 Practical Challenges; 4.4 Discussion; References; Chapter 5: Dealing with Uncertainty in the Analysis and Reporting of MCDA; 5.1 Introduction.

5.1.1 Problem Structuring5.1.2 Uncertainty in Problem Structuring; 5.2 Uncertainty in Scoring; 5.2.1 Performance Estimates; 5.2.2 From Performance to Value; 5.3 Uncertainty in Weighting; 5.4 Aggregation Methods; 5.5 Sensitivity Analysis; 5.6 Summary and Conclusions; References; Part II: Applications and Case Studies; Chapter 6: Supporting the Project Portfolio Selection Decision of Research and Development Investments by Means of Multi-Criteria Resource Allocation Modelling; 6.1 Introduction; 6.2 Case Study and Method; 6.2.1 Case Study.

Chapter 3: Identifying Value(s): A Reflection on the Ethical Aspects of MCDA in Healthcare Decisionmaking3.1 Introduction; 3.2 Who Should Decide? Legitimacy of Decisions and Representativeness of MCDA Users; 3.3 How to Decide?; 3.3.1 Step 1: Defining the Decision Problem; 3.3.2 Step 2: Selecting and Structuring Criteria; 3.3.2.1 Patient; 3.3.2.2 Population; 3.3.2.3 Healthcare Systems; 3.3.2.4 Knowledge and Context; 3.3.3 Step 3 of MCDA: Weighting Criteria; 3.3.4 Step 4 of MCDA: Providing Evidence to Measure Performance.

Representing the first collection on the topic, this book builds from foundations to case studies, to future prospects, providing the reader with a rich and comprehensive understanding of the use of multi-criteria decision analysis (MCDA) in healthcare. The first section of the collection presents the foundations of MCDA as it is applied to healthcare decisions, providing guidance on the ethical and theoretical underpinnings of MCDA and how to select MCDA methods appropriate to different decision settings. Section two comprises a collection of case studies spanning the decision continuum, including portfolio development, benefit-risk assessment, health technology assessment, priority setting, resource optimisation, clinical practice and shared decision making. Section three explores future directions in the application of MCDA to healthcare and identifies opportunities for further research to support these.