Martin P Ho1, Juan Marcos Gonzalez2, Herbert P Lerner3, Carolyn Y Neuland4, Joyce M Whang5, Michelle McMurry-Heath6,7, A Brett Hauber8, Telba Irony9. 1. Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Building 66, Room 2232, Silver Spring, MD, 20993-0002, USA. martin.ho@fda.hhs.gov. 2. RTI Health Solutions, Durham, USA. jgonzalez@rti.org. 3. Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Building 66, Room 2232, Silver Spring, MD, 20993-0002, USA. herbert.lerner@fda.hhs.gov. 4. Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Building 66, Room 2232, Silver Spring, MD, 20993-0002, USA. carolyn.neuland@fda.hhs.gov. 5. Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Building 66, Room 2232, Silver Spring, MD, 20993-0002, USA. joyce.whang@fda.hhs.gov. 6. Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Building 66, Room 2232, Silver Spring, MD, 20993-0002, USA. michelle.mcmurry-heath@fda.hhs.gov. 7. FaegreBD Consulting, Washington, USA. michelle.mcmurry-heath@fda.hhs.gov. 8. RTI Health Solutions, Durham, USA. abhauber@rti.org. 9. Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Building 66, Room 2232, Silver Spring, MD, 20993-0002, USA. telba.irony@fda.hhs.gov.
Abstract
BACKGROUND: Patients have a unique role in deciding what treatments should be available for them and regulatory agencies should take their preferences into account when making treatment approval decisions. This is the first study designed to obtain quantitative patient-preference evidence to inform regulatory approval decisions by the Food and Drug Administration Center for Devices and Radiological Health. METHODS: Five-hundred and forty United States adults with body mass index (BMI) ≥ 30 kg/m(2) evaluated tradeoffs among effectiveness, safety, and other attributes of weight-loss devices in a scientific survey. Discrete-choice experiments were used to quantify the importance of safety, effectiveness, and other attributes of weight-loss devices to obese respondents. A tool based on these measures is being used to inform benefit-risk assessments for premarket approval of medical devices. RESULTS: Respondent choices yielded preference scores indicating their relative value for attributes of weight-loss devices in this study. We developed a tool to estimate the minimum weight loss acceptable by a patient to receive a device with a given risk profile and the maximum mortality risk tolerable in exchange for a given weight loss. For example, to accept a device with 0.01 % mortality risk, a risk tolerant patient will require about 10 % total body weight loss lasting 5 years. CONCLUSIONS: Patient preference evidence was used make regulatory decision making more patient-centered. In addition, we captured the heterogeneity of patient preferences allowing market approval of effective devices for risk tolerant patients. CDRH is using the study tool to define minimum clinical effectiveness to evaluate new weight-loss devices. The methods presented can be applied to a wide variety of medical products. This study supports the ongoing development of a guidance document on incorporating patient preferences into medical-device premarket approval decisions.
BACKGROUND:Patients have a unique role in deciding what treatments should be available for them and regulatory agencies should take their preferences into account when making treatment approval decisions. This is the first study designed to obtain quantitative patient-preference evidence to inform regulatory approval decisions by the Food and Drug Administration Center for Devices and Radiological Health. METHODS: Five-hundred and forty United States adults with body mass index (BMI) ≥ 30 kg/m(2) evaluated tradeoffs among effectiveness, safety, and other attributes of weight-loss devices in a scientific survey. Discrete-choice experiments were used to quantify the importance of safety, effectiveness, and other attributes of weight-loss devices to obese respondents. A tool based on these measures is being used to inform benefit-risk assessments for premarket approval of medical devices. RESULTS: Respondent choices yielded preference scores indicating their relative value for attributes of weight-loss devices in this study. We developed a tool to estimate the minimum weight loss acceptable by a patient to receive a device with a given risk profile and the maximum mortality risk tolerable in exchange for a given weight loss. For example, to accept a device with 0.01 % mortality risk, a risk tolerant patient will require about 10 % total body weight loss lasting 5 years. CONCLUSIONS:Patient preference evidence was used make regulatory decision making more patient-centered. In addition, we captured the heterogeneity of patient preferences allowing market approval of effective devices for risk tolerant patients. CDRH is using the study tool to define minimum clinical effectiveness to evaluate new weight-loss devices. The methods presented can be applied to a wide variety of medical products. This study supports the ongoing development of a guidance document on incorporating patient preferences into medical-device premarket approval decisions.
Authors: Deborah Marshall; John F P Bridges; Brett Hauber; Ruthanne Cameron; Lauren Donnalley; Ken Fyie; F Reed Johnson Journal: Patient Date: 2010-12-01 Impact factor: 3.883
Authors: Ninh T Nguyen; Hossein Masoomi; Cheryl P Magno; Xuan-Mai T Nguyen; Kelly Laugenour; John Lane Journal: J Am Coll Surg Date: 2011-05-31 Impact factor: 6.113
Authors: John F P Bridges; A Brett Hauber; Deborah Marshall; Andrew Lloyd; Lisa A Prosser; Dean A Regier; F Reed Johnson; Josephine Mauskopf Journal: Value Health Date: 2011-04-22 Impact factor: 5.725
Authors: F Reed Johnson; Emily Lancsar; Deborah Marshall; Vikram Kilambi; Axel Mühlbacher; Dean A Regier; Brian W Bresnahan; Barbara Kanninen; John F P Bridges Journal: Value Health Date: 2013 Jan-Feb Impact factor: 5.725
Authors: Jimmy T Le; Amanda K Bicket; Ellen M Janssen; Davinder Grover; Sunita Radhakrishnan; Steven Vold; Michelle E Tarver; Malvina Eydelman; John F P Bridges; Tianjing Li Journal: Ophthalmol Glaucoma Date: 2019-09-03
Authors: Heather L Benz; Laura Rose; Okan Olgac; Karen Kreutz; Anindita Saha; Eugene F Civillico Journal: Conf Proc IEEE Eng Med Biol Soc Date: 2016-08
Authors: Heather L Benz; Ting-Hsuan Joyce Lee; Jui-Hua Tsai; John F P Bridges; Sara Eggers; Megan Moncur; Fadia T Shaya; Ira Shoulson; Erica S Spatz; Leslie Wilson; Anindita Saha Journal: Patient Date: 2019-12 Impact factor: 3.883