The value of quantitative patient preferences in regulatory benefit-risk assessment

References

• Health Equality Europe. Understanding Health Technology Assessment (HTA). 2008. [cited 2013 June 20]. Available from: http://img.eurordis.org/newsletter/pdf/nov-2010/58-1%20HEE%20Guide%20To%20HTA%20for%20Patients%20English.pdf .
   Google Scholar
• Facey K , Boivin A , Gracia J , Hansen HP , Scalzo AL , Ann Single JM . Patients’ perspectives in health technology assessment: A route to robust evidence and fair deliberation. Int J Technol Assess Health Care. 2010; 26(3): 334–40.
   Google Scholar
• Gagnon MP , Desmartis M , Lepage-Savary D , Gagnon J , Rhainds M , Lemieux M et al. . Introducing patients’ and the public's perspectives to health technology assessment: A systematic review of international experiences. Int J Technol Assess Health Care. 2011; 27(1): 31–42.
   Google Scholar
• Mussen F , Salek S , Walker S . Benefit-risk appraisal of medicines – A systematic approach to decision-making. 2009; Oxford, UK: Wiley-Blackwell.
   Google Scholar
• Whitty JA . An international survey of the public engagement practices of health technology assessment organizations. Value Health. 2013; 16(1): 155–63.
   Google Scholar
• Bridges JFP , Jones C . Patient-based health technology assessment: A vision of the future. Int J Technol Assess Health Care. 2007; 23(1): 30–5.
   Google Scholar
• Rowe G , Frewer L . A typology of public engagement mechanisms. Sci Tech Hum Val. 2005; 30(2): 251–90.
   Web of Science ®Google Scholar
• Gauvin FP . Public involvement in health technology assessment agencies: A comparative analysis of Canada, Denmark, and the United Kingdom. 2008; Hamilton, ON: McMaster University.
   Google Scholar
• Breckenridge A . Patient opinions and preferences in drug development and regulatory decision making. Drug Discov Today Technol. 2011; 8(1): e11–4.
   Google Scholar
• EMA. Report of the CHMP working group on benefit-risk assessment models and methods. 2007; London: EMA.
   Google Scholar
• EMA. Road map to 2015 – The European Medicines Agency's contribution to science, medicines and health. 2010; London: EMA.
   Google Scholar
• EMA. Fifth report on the interaction with patients’ and consumers’ organisations (2011). 2012; London: EMA.
   Google Scholar
• Johnson FR , Lancsar E , Marshall D , Kilambi V , Muhlbacher AC , Regier DA et al. . Constructing experimental designs for discrete-choice experiments: Report of the ISPOR conjoint analysis experimental design good research practices task force. Value Health. 2013; 16(2013): 3–13.
   Web of Science ®Google Scholar
• Marshall D , Bridges JFP , Hauber AB , Cameron R , Donnalley L , Fyie K et al. . Conjoint Analysis Applications in Health—How are Studies being Designed and Reported?. Patient. 2010; 3(4): 249–56.
   Google Scholar
• Bridges JFP , Hauber AB , Marshall D , Lloyd D , Prosser LA , Regier DA et al. . Conjoint analysis applications in health—A checklist: A report of the ISPOR good research practices for conjoint analysis task force. Value Health. 2011; 14(2011): 403–13.
   Google Scholar
• Sullivan T . Using MCDA (Multi-Criteria Decision Analysis) to prioritise publicly-funded health care. 2012; Dunedin, New Zealand: University of Otago.
   Google Scholar
• Bridges JFP . Stated preference methods in health care evaluation: An emerging methodological paradigm in health economics. Appl Health Econ Health Policy. 2003; 2(4): 213–24. [PubMed Abstract].
   Google Scholar
• Bridges JFP , Onukwugha E , Johnson FR , Hauber AB . Patient preference methods – A patient centered evaluation paradigm. ISPOR Connections 2007;13(6):4–7.
   Google Scholar
• EMA. Benefit-risk methodology project – Work package 2 report: Applicability of current tools and processes for regulatory benefit-risk assessment. 2010; London: EMA.
   Google Scholar
• Johnson FR , Ozdemir S , Hauber B , Kauf TL . Women's willingness to accept perceived risks for vasomotor symptom relief. J Women's Health. 2007; 16(7): 1028–40.
   Web of Science ®Google Scholar
• Hummel MJ , Volz F , Van Manen JG , Danner M , Dintsios C , Charalabos-Markos et al. . Using the analytic hierarchy process to elicit patient preferences: Prioritizing multiple outcome measures of antidepressant drug treatment. Patient. 2012; 5(4): 225–37. [PubMed Abstract].
   Google Scholar
• Danner M , Hummel MJ , Volz F , Van Manen JG , Wiegard B , Dintsios C et al. . Integrating patients’ views into health technology assessment: Analytic hierarchy process (AHP) as a method to elicit patient preferences. Int J Technol Assess Health Care. 2011; 27(4): 369–75.
   Google Scholar
• IJzerman MJ , van Til JA , Bridges JFP . A comparison of analytic hierarchy process and conjoint analysis methods in assessing treatment alternatives for stroke rehabilitation. Patient. 2012; 5(1): 45–56. [PubMed Abstract].
   Google Scholar
• Bowman-Busato J . Patient engagement in health technology assessment. Pharmaceuticals Policy and Law. 2011; 13(2011): 193–201.
   Google Scholar
• Hauber AB , Fairchild AO , Reed Johnson F . Quantifying benefit-risk preferences for medical interventions: An overview of a growing empirical literature. Appl Health Econ Health Policy. 2013; 11(4): 319–29.
   PubMedGoogle Scholar
• Broekhuizen H , Groothuis-Oudshoorn CGM , Hauber AB , Jansen JP , IJzerman MJ . Integrating elicited patient preferences and clinical trial data in a quantitative model for benefit-risk assessment. DIA 2013: The 25th Annual EuroMeeting of the Drug Information Association . 2013; March. 3–5
   Google Scholar
• Johnson FR . Between Regulatory Rocks and Hard Places: Quantifying Tolerance for Pharmaceutical Risks. Ispor Connections. 2008; 14(6): 6–8.
   Google Scholar