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
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 QLUC10D
• The Australian Utility Scoring algorithm
• Finding a home for the QLU-C10D
• Future directions
The University of Sydney
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QLQ-C30 is a HRQoL Profile Measure
Illustrated with the General Norwegian Population, Hjermstad JCO 1998
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The University of Sydney
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HRQoL Profile v Preference-based (Utility) Measures
Profile measures
Preference-based 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 0-100
range
– 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
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
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Multi-attribute utility instrument
Part 1
Health state classification system
= n domains x m levels
Simplest example:
2 domains
x 2 levels
Emotional
Physical
Poor
Good
Poor
HS1
HS2
Good
HS3
HS4
The University of Sydney
Part 2
Utility scoring algorithm
Utility = f (w1Domain1, w2HDomain2)
Anchored: Full health = 1, Death = 0
U (HS4) = 1
U (HS3) = 1 – disutility (poor physical)
U (HS2) = 1 – disutility (poor emotional)
U (HS1) = 1 – disutility (poor physical,
poor emotional)
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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
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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
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
Introducing the MAUCa
Consortium
The University of Sydney
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Multi-Attribute Utility in Cancer
MAUCa Consortium members
Madeleine King (Chair)
7 Australians
Neil Aaronson, John Brazier,
4 EORTC QOL Group
David Cella, Dan Costa,
members
Peter Fayers, Peter Grimison,
4 Nth Americans
Monika Janda, Georg Kemmler
3 Univ Sheffield
Helen McTaggart-Cowan,
Richard Norman, Julie Pallant,
8 health economists
Stuart Peacock, Simon Pickard, 5 statisticians/psychometricians
Donna Rowen, Galina Velikova,
2 oncologists
Rosalie Viney, Tracey Young 2 HRQOL assessment specialists
1 behavioural scientists
The University of Sydney
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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
Funding acknowledgments
– Australian National Health & Medical Research Council
Project Grant 2010-2015
– 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
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