TY - JOUR
T1 - Suitability of Preference Methods Across the Medical Product Lifecycle
T2 - A Multicriteria Decision Analysis
AU - Veldwijk, Jorien
AU - de Bekker-Grob, Esther
AU - Juhaeri, Juhaeri
AU - van Overbeeke, Eline
AU - Tcherny-Lessenot, Stephanie
AU - Pinto, Cathy Anne
AU - DiSantostefano, Rachael L.
AU - Groothuis-Oudshoorn, Catharina G.M.
N1 - Funding Information:
Funding/Support: This study formed part of the PREFER project. The PREFER project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no. 115966. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations.
Funding Information:
Funding/Support: This study formed part of the PREFER project. The PREFER project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no. 115966 . This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations .
Publisher Copyright:
© 2022
PY - 2023/4/1
Y1 - 2023/4/1
N2 - Objectives: This study aimed to understand the importance of criteria describing methods (eg, duration, costs, validity, and outcomes) according to decision makers for each decision point in the medical product lifecycle (MPLC) and to determine the suitability of a discrete choice experiment, swing weighting, probabilistic threshold technique, and best-worst scale cases 1 and 2 at each decision point in the MPLC. Methods: Applying multicriteria decision analysis, an online survey was sent to MPLC decision makers (ie, industry, regulatory, and health technology assessment representatives). They ranked and weighted 19 methods criteria from an existing performance matrix about their respective decisions across the MPLC. All criteria were given a relative weight based on the ranking and rating in the survey after which an overall suitability score was calculated for each preference elicitation method per decision point. Sensitivity analyses were conducted to reflect uncertainty in the performance matrix. Results: Fifty-nine industry, 29 regulatory, and 5 health technology assessment representatives completed the surveys. Overall, “estimating trade-offs between treatment characteristics” and “estimating weights for treatment characteristics” were highly important criteria throughout all MPLC decision points, whereas other criteria were most important only for specific MPLC stages. Swing weighting and probabilistic threshold technique received significantly higher suitability scores across decision points than other methods. Sensitivity analyses showed substantial impact of uncertainty in the performance matrix. Conclusion: Although discrete choice experiment is the most applied preference elicitation method, other methods should also be considered to address the needs of decision makers. Development of evidence-based guidance documents for designing, conducting, and analyzing such methods could enhance their use.
AB - Objectives: This study aimed to understand the importance of criteria describing methods (eg, duration, costs, validity, and outcomes) according to decision makers for each decision point in the medical product lifecycle (MPLC) and to determine the suitability of a discrete choice experiment, swing weighting, probabilistic threshold technique, and best-worst scale cases 1 and 2 at each decision point in the MPLC. Methods: Applying multicriteria decision analysis, an online survey was sent to MPLC decision makers (ie, industry, regulatory, and health technology assessment representatives). They ranked and weighted 19 methods criteria from an existing performance matrix about their respective decisions across the MPLC. All criteria were given a relative weight based on the ranking and rating in the survey after which an overall suitability score was calculated for each preference elicitation method per decision point. Sensitivity analyses were conducted to reflect uncertainty in the performance matrix. Results: Fifty-nine industry, 29 regulatory, and 5 health technology assessment representatives completed the surveys. Overall, “estimating trade-offs between treatment characteristics” and “estimating weights for treatment characteristics” were highly important criteria throughout all MPLC decision points, whereas other criteria were most important only for specific MPLC stages. Swing weighting and probabilistic threshold technique received significantly higher suitability scores across decision points than other methods. Sensitivity analyses showed substantial impact of uncertainty in the performance matrix. Conclusion: Although discrete choice experiment is the most applied preference elicitation method, other methods should also be considered to address the needs of decision makers. Development of evidence-based guidance documents for designing, conducting, and analyzing such methods could enhance their use.
KW - decision makers
KW - medical product lifecycle
KW - multicriteria decision analysis
KW - preference elicitation
KW - preference methods
KW - stakeholders
UR - https://www.scopus.com/pages/publications/85146059709
U2 - 10.1016/j.jval.2022.11.019
DO - 10.1016/j.jval.2022.11.019
M3 - Article
C2 - 36509368
AN - SCOPUS:85146059709
SN - 1098-3015
VL - 26
SP - 579
EP - 588
JO - Value in health
JF - Value in health
IS - 4
ER -
