R Norman1,2, R Viney3, N K Aaronson4, J E Brazier5, D Cella6, D S J Costa7, P M Fayers8,9, G Kemmler10, S Peacock11,12,13, A S Pickard14, D Rowen5, D J Street15, G Velikova16,17, T A Young5, M T King7,18. 1. School of Public Health, Curtin University, Perth, Australia. Richard.norman@curtin.edu.au. 2. Centre for Health Economics Research and Evaluation (CHERE), University of Technology Sydney (UTS), Sydney, Australia. Richard.norman@curtin.edu.au. 3. Centre for Health Economics Research and Evaluation (CHERE), University of Technology Sydney (UTS), Sydney, Australia. 4. The Netherlands Cancer Institute, Amsterdam, The Netherlands. 5. School of Health and Related Research, University of Sheffield, Sheffield, South Yorkshire, UK. 6. Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. 7. Psycho-Oncology Cooperative Research Group (PoCoG), University of Sydney, Sydney, Australia. 8. Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK. 9. Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. 10. Innsbruck Medical University, Innsbruck, Austria. 11. Faculty of Health Sciences, Simon Fraser University, Vancouver, Canada. 12. Canadian Centre for Applied Research in Cancer Control (ARCC), Vancouver, Canada. 13. British Columbia Cancer Agency, Vancouver, Canada. 14. Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA. 15. School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, Australia. 16. Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK. 17. St James's Hospital, Leeds, UK. 18. Central Clinical School, Sydney Medical School, University of Sydney, Sydney, Australia.
Abstract
PURPOSE: To assess the feasibility of using a discrete choice experiment (DCE) to value health states within the QLU-C10D, a utility instrument derived from the QLQ-C30, and to assess clarity, difficulty, and respondent preference between two presentation formats. METHODS: We ran a DCE valuation task in an online panel (N = 430). Respondents answered 16 choice pairs; in half of these, differences between dimensions were highlighted, and in the remainder, common dimensions were described in text and differing attributes were tabulated. To simplify the cognitive task, only four of the QLU-C10D's ten dimensions differed per choice set. We assessed difficulty and clarity of the valuation task with Likert-type scales, and respondents were asked which format they preferred. We analysed the DCE data by format with a conditional logit model and used Chi-squared tests to compare other responses by format. Semi-structured telephone interviews (N = 8) explored respondents' cognitive approaches to the valuation task. RESULTS: Four hundred and forty-nine individuals were recruited, 430 completed at least one choice set, and 422/449 (94 %) completed all 16 choice sets. Interviews revealed that respondents found ten domains difficult but manageable, many adopting simplifying heuristics. Results for clarity and difficulty were identical between formats, but the "highlight" format was preferred by 68 % of respondents. Conditional logit parameter estimates were monotonic within domains, suggesting respondents were able to complete the DCE sensibly, yielding valid results. CONCLUSION: A DCE valuation task in which only four of the QLU-C10D's ten dimensions differed in any choice set is feasible for deriving utility weights for the QLU-C10D.
PURPOSE: To assess the feasibility of using a discrete choice experiment (DCE) to value health states within the QLU-C10D, a utility instrument derived from the QLQ-C30, and to assess clarity, difficulty, and respondent preference between two presentation formats. METHODS: We ran a DCE valuation task in an online panel (N = 430). Respondents answered 16 choice pairs; in half of these, differences between dimensions were highlighted, and in the remainder, common dimensions were described in text and differing attributes were tabulated. To simplify the cognitive task, only four of the QLU-C10D's ten dimensions differed per choice set. We assessed difficulty and clarity of the valuation task with Likert-type scales, and respondents were asked which format they preferred. We analysed the DCE data by format with a conditional logit model and used Chi-squared tests to compare other responses by format. Semi-structured telephone interviews (N = 8) explored respondents' cognitive approaches to the valuation task. RESULTS: Four hundred and forty-nine individuals were recruited, 430 completed at least one choice set, and 422/449 (94 %) completed all 16 choice sets. Interviews revealed that respondents found ten domains difficult but manageable, many adopting simplifying heuristics. Results for clarity and difficulty were identical between formats, but the "highlight" format was preferred by 68 % of respondents. Conditional logit parameter estimates were monotonic within domains, suggesting respondents were able to complete the DCE sensibly, yielding valid results. CONCLUSION: A DCE valuation task in which only four of the QLU-C10D's ten dimensions differed in any choice set is feasible for deriving utility weights for the QLU-C10D.
Entities:
Keywords:
Cancer; Discrete choice experiment; QLQ-C30; Quality of life; Utility
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