Leveraging Patient Preference Information in Medical Device Clinical Trial Design
Name
43441_2022_Article_450.pdf
Size
525.9 KB
Format
Adobe PDF
Checksum (MD5)
62e255691c1758f51d878828163f10c8
Author(s)
Rincon-Gonzalez, Liliana
Selig, Wendy K. D.
Hauber, Brett
Reed, Shelby D.
Tarver, Michelle E.
Chaudhuri, Shomesh E.
Lo, Andrew W.
Bruhn-Ding, Dean
Liden, Barry
Date Issued
August 27, 2022
Publisher
Springer International Publishing
Citation
Rincon-Gonzalez, Liliana, Selig, Wendy K. D., Hauber, Brett, Reed, Shelby D., Tarver, Michelle E. et al. 2022. "Leveraging Patient Preference Information in Medical Device Clinical Trial Design."
Version
Final published version
Abstract
Abstract
Use of robust, quantitative tools to measure patient perspectives within product development and regulatory review processes offers the opportunity for medical device researchers, regulators, and other stakeholders to evaluate what matters most to patients and support the development of products that can best meet patient needs. The medical device innovation consortium (MDIC) undertook a series of projects, including multiple case studies and expert consultations, to identify approaches for utilizing patient preference information (PPI) to inform clinical trial design in the US regulatory context. Based on these activities, this paper offers a cogent review of considerations and opportunities for researchers seeking to leverage PPI within their clinical trial development programs and highlights future directions to enhance this field. This paper also discusses various approaches for maximizing stakeholder engagement in the process of incorporating PPI into the study design, including identifying novel endpoints and statistical considerations, crosswalking between attributes and endpoints, and applying findings to the population under study. These strategies can help researchers ensure that clinical trials are designed to generate evidence that is useful to decision makers and captures what matters most to patients.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Sloan School of Management. Laboratory for Financial Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s43441-022-00450-9
