Introducing the QLU-C10D A preference-based utility measure derived from the QLQ-C30

Transcription

1 Introducing the QLU-C10D A preference-based utility measure derived from the QLQ-C30 Presented by Professor Madeleine King Cancer Australia Chair in Cancer Quality of Life Faculties of Science and Medicine EORTC QOL Group Fall Meeting 2015 Krakow Polad 11 Sept The University of Sydney Page 1

2 OVERVIEW Topics HRQoL Profile vs Preference-based (Utility) measures Multi-Attribute Utility Instuments (MAUIs) Multi-Attribute Utility in Cancer (MAUCa) How we derived the health state classification system for the QLU- C10D The Australian Utility Scoring algorithm Finding a home for the QLU-C10D Future directions The University of Sydney Page 2

3 QLQ-C30 is a HRQoL Profile Measure Illustrated with the General Norwegian Population, Hjermstad JCO 1998 Best 100 Most QOL Worst Functional & Global Scales Phys Role Emot Social Cogn Global Symptom Scales Nausea Pain Fatigue Symptoms Least The University of Sydney Page 3 3

4 HRQoL Profile v Preference-based (Utility) Measures Profile measures Psychometric approach A respondent s describes his/her own HRQoL Domains of HRQoL are kept separate, a series of domain scores describe a HRQoL profile Each domain score is typically derived from several items (questions) Typically items (questions) are unweighted Ordinal scales No anchors: minimum and maximum values are a function of scoring algorithm Typically average or sum of items Often linearly transformed to range Preference-based measures Utility approach Scores represent the value of health states Domains of HRQoL are combined into a single index Typically one item (question) per domain Domains are weighted Weights are derived using a preference-based method capturing community values and preferences / trade-offs across domains of HRQoL and relative to survival duration Interval scale, anchored at 0 (death) and 1 (full health), values worse than death are possible The University of Sydney Page 4

5 Background Increasing use of cost-utility analysis (CUA) to inform health technology assessment and reimbursement decision making Increasingly expensive cancer treatments value for money? CUA is a cost-effectiveness analysis that uses utility as a health outcome metric to weight survival, typically as the Quality Adjusted Life Year (QALY) For CUA, QOL data must be collected in a form that allows QALYs to be calculated Multi-attribute utility instruments are a good way to do this The University of Sydney Page 5

6 Multi-attribute utility instrument Part 1 Health state classification system = n domains x m levels Part 2 Utility scoring algorithm Simplest example: 2 domains x 2 levels Physical Poor Good Utility = f (w 1 Domain 1, w 2 HDomain 2 ) Anchored: Full health = 1, Death = 0 Emotional Poor HS1 HS2 Good HS3 HS4 U (HS4) = 1 U (HS3) = 1 disutility (poor physical) U (HS2) = 1 disutility (poor emotional) U (HS1) = 1 disutility (poor physical, poor emotional) The University of Sydney Page 6

7 Multi-Attribute Utility Instruments Can be stand-alone EQ-5D, HUI, AQoL Generic Can be derived from a HRQoL profile measure E.g. Generic SF-6D, derived from SF-36 or SF-12 E.g. Cancer-specific QLU-C10D, derived from QLQ-C30 The University of Sydney Page 7

8 Why would we want a disease specific MAUI? Disease-specific HRQoL profile measures tend to be more sensitive than generic HRQoL profile measures Weibe et al, JCE 2003 Generic MAUIs may not be as sensitive as disease-specific MAUIs In cancer fatigue, nausea, gastro-intestinal issues stand out as important In the health care reimbursement context, understanding the QALY impacts of disease-specific issues is important Especially in the context of new expensive treatments The University of Sydney Page 8

9 Why derive a MAUI from the QLQ-C30? May be more sensitive than generic MAUI If we can use the QLQ-C30 data to calculate QALYs, saves patient burden of additional questionnaires (e.g. EQ-5D) Can be applied retrospectively to a huge body of evidence The University of Sydney Page 9

10 Introducing the MAUCa Consortium The University of Sydney Page 10

11 Multi-Attribute Utility in Cancer MAUCa Consortium members Madeleine King (Chair) Neil Aaronson, John Brazier, David Cella, Dan Costa, Peter Fayers, Peter Grimison, Monika Janda, Georg Kemmler Helen McTaggart-Cowan, Richard Norman, Julie Pallant, Stuart Peacock, Simon Pickard, Donna Rowen, Galina Velikova, Rosalie Viney, Tracey Young 7 Australians 4 EORTC QOL Group members 4 Nth Americans 3 Univ Sheffield 8 health economists 5 statisticians/psychometricians 2 oncologists 2 HRQOL assessment specialists 1 behavioural scientists The University of Sydney Page 11

12 Aims of the MAUCa project Develop a MAUI which can be derived from the QLQ-C30 and the FACT-G Develop a health state classification system from select domains and items of the QLQ-C30 and the FACT-G Derive a utility algorithms for the new MAUIs Develop a preference elicitation method suitable for determining country-specific utility weights Conduct valuation surveys by country Model health state values Develop algorithms for scoring country-specific utility weights The University of Sydney Page 12

13 Funding acknowledgments Australian National Health & Medical Research Council Project Grant Stage 1 - health state classification systems for QLQ-C30 and FACT-G Stage 2 - Australian QALY weights + Australian general population values for QLQ-C30 & FACT-G MAUCa members to develop international funding applications for country-specific studies Focus on compatibility of research methods with potential to pool/compare international data sets The University of Sydney Page 13