Abstract
Objectives
To develop a value set reflecting the United States (US) general population’s preferences for health states described by the Functional Assessment of Cancer Therapy (FACT) eight-dimensions preference-based multi-attribute utility instrument (FACT-8D), derived from the FACT-General cancer-specific health-related quality-of-life (HRQL) questionnaire.
Methods
A US online panel was quota-sampled to achieve a general population sample representative by sex, age (≥ 18 years), race and ethnicity. A discrete choice experiment (DCE) was used to value health states. The valuation task involved choosing between pairs of health states (choice-sets) described by varying levels of the FACT-8D HRQL dimensions and survival (life-years). The DCE included 100 choice-sets; each respondent was randomly allocated 16 choice-sets. Data were analysed using conditional logit regression parameterized to fit the quality-adjusted life-year framework, weighted for sociodemographic variables that were non-representative of the US general population. Preference weights were calculated as the ratio of HRQL-level coefficients to the survival coefficient.
Results
2562 panel members opted in, 2462 (96%) completed at least one choice-set and 2357 (92%) completed 16 choice-sets. Pain and nausea were associated with the largest utility weights, work and sleep had more moderate utility weights, and sadness, worry and support had the smallest utility weights. Within dimensions, more severe HRQL levels were generally associated with larger weights. A preference-weighting algorithm to estimate US utilities from responses to the FACT-General questionnaire was generated. The worst health state’s value was −0.33.
Conclusions
This value set provides US population utilities for health states defined by the FACT-8D for use in evaluating oncology treatments.
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Details
; Revicki, D. A. 2
; Norman, R. 3
; Müller, F. 4
; Viney, R.C. 5
; Pickard, A. S. 6
; Cella, D. 7
; Shaw, J. W. 8 ; Aaronson, Neil; Brazier, John; Cella, David; Costa, Daniel; Fayers, Peter; Grimison, Peter; Janda, Monika; Kemmler, Georg; Luo, Nan; McTaggart-Cowan, Helen; Mercieca-Bebber, Rebecca; Norman, Richard; Revicki, Dennis; Peacock, Stuart; Pickard, Simon; Rowen, Donna; Velikova, Galina; Viney, Rosalie; Street, Deborah; Young, Tracey; Tait, Margaret-Ann1 University of Sydney, School of Psychology, Faculty of Science, Sydney, Australia (GRID:grid.1013.3) (ISNI:0000 0004 1936 834X)
2 Revicki Outcomes Research Consulting, Sarasota, USA (GRID:grid.1013.3)
3 Curtin University, School of Population Health, Perth, Australia (GRID:grid.1032.0) (ISNI:0000 0004 0375 4078)
4 Amsterdam UMC Location University of Amsterdam, Medical Psychology, Amsterdam, The Netherlands (GRID:grid.7177.6) (ISNI:0000 0000 8499 2262)
5 University of Technology Sydney, Centre for Health Economics Research & Evaluation, Faculty of Health, Sydney, Australia (GRID:grid.117476.2) (ISNI:0000 0004 1936 7611)
6 University of Illinois at Chicago, Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, Chicago, USA (GRID:grid.185648.6) (ISNI:0000 0001 2175 0319)
7 Northwestern University Feinberg School of Medicine, Department of Medical Social Sciences, Chicago, USA (GRID:grid.16753.36) (ISNI:0000 0001 2299 3507)
8 Global Health Economics and Outcomes Research, Patient-Reported Outcomes Assessment, Lawrenceville, USA (GRID:grid.16753.36)





