Synopsis
I. Introduction.- 1. Introducing Decision Making in a World of Comparative Effectiveness Research.- 2. Perspectives on Decision Making in a World of Comparative Effectiveness: Views from Diverse Constituencies.- II. The Future of CER for Evidence Developers: Perspectives from Pharmaceutical Decision Makers.- 3. Perspectives on the Use of CER by Life Sciences Executives: An Interview with Mike Bonney.- 4. Perspectives on the Use of CER by Life Sciences Executives: An Interview with a Senior Executive at an International Life Science Company.- 5. CER: A Pharmaceutical Industry Perspective on Outlook, Dilemmas, and Controversies.- 6. Impact of Comparative Effectiveness Research on Drug Development Strategy and Innovation.- 7. Pricing of Pharmaceuticals: Current Trends and Outlook, and the Role of CER.- III. Evolving Stakeholder Considerations: Patients, Physicians, Regulators and Payers.- 8. Are Real-World Data and Evidence Good Enough to Inform Healthcare and Health Policy Decision Making?.- 9. Translating CER Evidence to Real-World Decision-Making: Some Practical Considerations.- 10. Decision Making by Public Payers.- 11. Patient and Stakeholder Engagement in Designing Pragmatic Clinical Trials.- 12. Policy Considerations: Ex-U.S. Payers and Regulators.- 13. Perspectives on the Common Drug Review (CDR) Process at the Canadian Agency for Drugs and Technologies in Health (CADTH).- 14. Evaluating Non-Pharmaceutical Technologies at the Canadian Agency for Drugs and Technologies in Health (CADTH).- 15. Challenges and Opportunities in the Dissemination of CER Information to Physicians and Payers: A Legal Perspective.- 16. Legal Considerations in a World of CER.- IV. Emerging Challenges, Methods and Applications of CER: Real-world and Big Data.- 17. Application of CER to Promote Adherence to Clinical Practice Guidelines.- 18. Challenges in Developing and Assessing CER for Medical Technology.- 19. Evidence Generation Using Big Data: Challenges and Opportunities.- 20. Indirect Comparisons Using Clinical Trial Data: 5 Years Later.- 21. Decision-Making with Machine Learning in Our Modern, Data-Rich Healthcare Industry.
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