PURPOSE: Multiple criteria decision analysis swing weighting (SW) and discrete choice experiments (DCE) are appropriate methods for capturing patient preferences on treatment benefit-risk trade-offs. This paper presents a qualitative comparison of the 2 methods. METHODS: We review and critically assess similarities and differences of SW and DCE based on 6 aspects: comprehension by study participants, cognitive biases, sample representativeness, ability to capture heterogeneity in preferences, reliability and validity, and robustness of the results. RESULTS: The SW choice task can be more difficult, but the workshop context in which SW is conducted may provide more support to patients who are unfamiliar with the end points being evaluated or who have cognitive impairments. Both methods are similarly prone to a number of biases associated with preference elicitation, and DCE is prone to simplifying heuristics, which limits its application with large number of attributes. The low cost per patient of the DCE means that it can be better at achieving a representative sample, though SW does not require such large sample sizes due to exact nature of the collected preference data. This also means that internal validity is automatically enforced with SW, while the internal validity of DCE results needs to be assessed manually. CONCLUSIONS: Choice between the 2 methods depends on characteristics of the benefit-risk assessment, especially on how difficult the trade-offs are for the patients to make and how many patients are available. Although there exist some empirical studies on many of the evaluation aspects, critical evidence gaps remain.
PURPOSE: Multiple criteria decision analysis swing weighting (SW) and discrete choice experiments (DCE) are appropriate methods for capturing patient preferences on treatment benefit-risk trade-offs. This paper presents a qualitative comparison of the 2 methods. METHODS: We review and critically assess similarities and differences of SW and DCE based on 6 aspects: comprehension by study participants, cognitive biases, sample representativeness, ability to capture heterogeneity in preferences, reliability and validity, and robustness of the results. RESULTS: The SW choice task can be more difficult, but the workshop context in which SW is conducted may provide more support to patients who are unfamiliar with the end points being evaluated or who have cognitive impairments. Both methods are similarly prone to a number of biases associated with preference elicitation, and DCE is prone to simplifying heuristics, which limits its application with large number of attributes. The low cost per patient of the DCE means that it can be better at achieving a representative sample, though SW does not require such large sample sizes due to exact nature of the collected preference data. This also means that internal validity is automatically enforced with SW, while the internal validity of DCE results needs to be assessed manually. CONCLUSIONS: Choice between the 2 methods depends on characteristics of the benefit-risk assessment, especially on how difficult the trade-offs are for the patients to make and how many patients are available. Although there exist some empirical studies on many of the evaluation aspects, critical evidence gaps remain.
Authors: Gaelle Saint-Hilary; Veronique Robert; Mauro Gasparini; Thomas Jaki; Pavel Mozgunov Journal: Stat Methods Med Res Date: 2018-07-20 Impact factor: 3.021
Authors: Rosanne Janssens; Tamika Lang; Ana Vallejo; Jayne Galinsky; Ananda Plate; Kate Morgan; Elena Cabezudo; Raija Silvennoinen; Daniel Coriu; Sorina Badelita; Ruxandra Irimia; Minna Anttonen; Riikka-Leena Manninen; Elise Schoefs; Martina Vandebroek; Anneleen Vanhellemont; Michel Delforge; Hilde Stevens; Steven Simoens; Isabelle Huys Journal: Front Med (Lausanne) Date: 2021-07-06
Authors: Maureen Rutten-van Mölken; Fenna Leijten; Maaike Hoedemakers; Apostolos Tsiachristas; Nick Verbeek; Milad Karimi; Roland Bal; Antoinette de Bont; Kamrul Islam; Jan Erik Askildsen; Thomas Czypionka; Markus Kraus; Mirjana Huic; János György Pitter; Verena Vogt; Jonathan Stokes; Erik Baltaxe Journal: BMC Health Serv Res Date: 2018-07-24 Impact factor: 2.655