Decision Making in a World of Comparative Effectiveness Research (eBook)

Dr. Birnbaum is a Principal at Analysis Group. Dr. Birnbaum’s professional interest is performing economic investigations regarding the comparative effectiveness and value of medical services and products and health economics of specific conditions using real world data from administrative claims files, medical records, and surveys of patients and physicians, as well as clinical trial data. He has conducted health economics and outcomes research across a broad range of physical and mental disorders for pharmaceutical and biotechnology manufacturers, managed care organizations, and government agencies, as well as provided expert testimony and litigation support in matters regarding Medicare and Medicaid reimbursement and managed care issues. Dr. Birnbaum has published numerous articles in leading medical, health economics, and managed care journals on a wide range of conditions. Dr. Birnbaum also serves on the editorial board of PharmacoEconomics, and is a member of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Institutional Council.Mr. Greenberg is the Director of Analysis Group's Health Care Practice and specializes in health economics, both in health outcomes research and in complex business litigation. He consults to pharmaceutical, biotechnology, and medical device companies concerning the health economics profiles of specific diseases and medical products. Mr. Greenberg has overseen cost-of-illness research relating to numerous physical and psychiatric disorders, as well as pharmacoeconomic assessments of the cost-effectiveness of drugs based on data gathered in clinical trials and administrative claims files.  Mr. Greenberg also draws upon the methodological tools and data sources used in health outcomes research to analyze problems that emerge in litigation. He currently serves on the editorial boards of PharmacoEconomics, Journal of Medical Economics, and Expert Opinion on Pharmacotherapy.

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.