Deriving distributional weights for Quality Adjusted Life Years (QALYs) through discrete choice experiments

Emily LancsarAssociate Professor

Centre for Health EconomicsMonash University

New Directions in Welfare7 July 2011

Acknowledgements

• ESRC/MRC/NIHR Fellowship in Economics of Health

• Joint work with John Wildman, Cam Donaldson, Mandy Ryan and Rachel Baker

QALYs• Scarcity – need to prioritize what is funded/covered

economic evaluation• Quality adjusted life year (QALY) dominant measure of benefit

assessment in economic evaluation in the health sector• QALY is LE x QOL; 1 QALY = 1 year in full health• Wide spread use internationally in HTA – e.g.

– NICE in the UK – PBAC in Australia– CADTH in Canada

In England…• When this study was undertaken….• NICE appraises new & existing health technologies, makes

recommendations to rest of NHS • Requires judgments about value of QALY gains • The mythical £30,000 per QALY!!• Is it in the right ballpark?• What determines whether something above the ‘threshold’

might be accepted?• Even without money values, what weights might be attached

to QALY gains in different situations?• These questions led to the Social Value of a QALY (SVQ)

project

• Funded by Dept of Health’s NCCRM, 2004-2007

• Team: Ian Bateman1, Cam Donaldson2, Michael Jones-Lee2, Emily Lancsar2, Graham Loomes1, Helen Mason2, Jose Luis Pinto Prades1 Angela Robinson1, Mandy Ryan3, Phil Shackley2, Richard Smith1, Kerry Sproston4,

Robert Sugden1, Heather Wardle4, John Wildman2

1. University of East Anglia2. Newcastle University3. University of Aberdeen4. National Centre for Social Research (NatCen)

SVQ

SVQ: Key research questions

1. What is the social value of a QALY (to inform consideration of cost effectiveness thresholds)?

2. Does this value differ depending on the characteristics of the recipients of such health gains?

Distributional Weights• Default normative framework: all QALYs are equal (“a

QALY is a QALY is a QALY”); maximise total• Alternative: Would members of the public prefer

policy makers to trade off health gains for other characteristics when allocating scarce health care resources?

• If so, which characteristics of recipients of QALYs warrant differential treatment?

• What weight should they receive?

DCEs – what are they?• Three components:

1. Surveys used to collect choice data using an experimental design

2. Discrete choice analysis used to model preferences from the generated data in a random utility framework

3. Model of preferences (IUF) used to derive welfare measures & other policy analysis

Key methodological challenges of this study

1. Identifying attributes over which weights should be derived

2. Designing & presenting questions so that respondents can understand & make complex choices

3. Eliciting quantitative preference data from members of the general public to allow the estimation of distributional weights

4. Developing a way to derive weights from the model of preferences

Qualitative Research

• One year of qualitative work to:– establish attributes– develop & pilot the questionnaire

• 17 focus groups• 1 on 1 cognitive interviews• Q sort

Attributes, Levels & Experimental DesignDescription of attribute LevelsAge at onset (years) 1 10 20 40 60 70Age at death if untreated (yrs) 1 10 20 40 60 70 80Severity: Qol if untreated (%) 0 30 60 90Gain in Qol with treatment (%) 0 10 20 40 70 100Gain in life expectancy (yrs) 0 1 5 10 20 40 60 79

• ED – combination of attributes and levels to present as choice options• Implausible combinations → constraints → compromised statistical

efficiency • However, efficiency is relative to an optimal design without

implausible scenarios; optimal design for this study is not known• Increased realism and “respondent efficiency”

Choice context• Choice of who to treat out of two types of patients A & B

• For each patient type, described what would happen first without treatment & then with treatment

• Context: two options cost the same & treat the same number of patients, but fixed amount of resources making choice necessary - invoking consideration of a government budget constraint

• ‘Citizen’ perspective - lives of others

Presenting Choices• For each option A & B:

• First depict scenario without treatment: full health from birth until illness at some age depicted as reduction in either QOL or LE axis or both (light blue)

• Next depict gain with treatment: QOL gain, LE gain or combination of both (dark blue)

• Two stage presentation – untreated situation useful:– starting point to demonstrate potential health gain – to show counterfactual of what happens to group not

chosen

Data collection• National Centre for Social Research• Face to face in home using CAPI• Questionnaire: background, DCE, PTO, attitudinal

questions & socio-demographics• 587 members of adult population in England – 243 (41%) were male (compared to 49% in the general

population)– mean age: 52 years (47 in the general adult population)

Discrete choice model• V=f(AO, AD, QL, QALY)

• Create a QALY variable: gain LE x QOL• Cannot be additive (to avoid positive utility even when

QALYs=0)• Assumed multiplicative underlying model – log-linear model &

accounted for higher order non linearities

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))log(()log(())log((

))log(())log(())log((

))log(())log(())log((

))log(())log(())log(()log(

QALYQALYQALY

QLQLQL

ADADAD

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Choice Model Results• AO not significant• AD significant, increasing at decreasing rate• Severity significant only at 10% in the linear term,

preference for treating individuals in less severe states

• Increasing QALYs always preferred

Main result: QALYs dominate the model in terms of size of coefficient & statistical significance

Two types of distributional weights1. Weights for individual characteristics (AO, AD,

severity)– Calculated by changing one characteristic at a time,

holding all else constant

2. Weights for whole scenarios, or combinations of characteristics, describing beneficiaries of QALY gains – Derived for 16 types of beneficiaries each receiving 1, 2, 4

& 10 QALYs with treatment (64 cases)

Deriving Distributional Weights• Hicksian compensating variation from a discrete

choice random utility model:

• Traditionally used to calculate monetary value• Instead, value the change of interest using marginal

utility of a QALY (for a 1 QALY gain) rather than MUy

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j

VJ

j

V jj eeCV11

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lnln1

Deriving distributional weights

• CV values used to calculate weights:

• CV: number of QALYs required to equalise expected utility in the reference & alternative case

• QALYbase: number of QALYs gained in the reference case • Weight greater (less) than one indicates the alternative case is valued

more (less) highly than the reference

baseQALYCVWeight 1

Weights for individual characteristics• AO: No weight is given based on age of onset of illness

• AD: Preference for giving more weight (although weight very small) to those who die at 10, 70 or 80 – may reflect caring about the very young & old in our society

• Severity: less weight given to the most severe groups relative to less severe– In line with Dolan and Tsuchiya (2005)– May be giving 4 QALYs to those already experiencing very poor QOL

does not seem as useful as giving QALYs to individuals who are more likely to be returned closer to full health (capacity to benefit?)

Weights for beneficiary types (constant QALYs)

• Very little preference for weighting QALYs• Where weights significant, small (range 0.89 to 1.14)• Significant weight associated with giving QALYs to 1

year olds who without treatment die at age 10 & either face a QOL loss of 0.1 or 0.7

• Preference for 10 year olds over infants – possibly related to argument that the latter have not really yet engaged with life whereas the former have? “Golden age”?

Weights for beneficiary types (varying QALYs)

• Trade off number of QALYs against other characteristics

• In aggregate, all weights are relatively small & number of QALYs gained is driving the results

• Exceptions involve 10 year olds

• Suggests a desire to maximise health & a reluctance to trade off health gain for other characteristics as the health gain increased

End of Life Weights• Policy issue: should more weight be given to treating

those people who are at the end of their life at any age & in particular when this might be considered premature?

• Our results suggest individuals facing instant & premature death should not be given a higher weighting

• In fact, given a lower weighting relative to the reference case

Conclusions & Contributions

• Generally both sets of weights suggest little evidence on which to weight QALYs based on the characteristics of the recipients; where significant, much smaller than PTO lit

• New analytical approach: DCE & novel application of Hicksian compensating variation

• Methodological challenges• Derivation of weights for QALYs, not just for life or life years

saved• Investigation of the impact of the size of the health gain by

allowing the gain to be traded against other characteristics • Additional, albeit imperfect, evidence to inform the debate on

the need or otherwise to weight QALYs

Thank you & Questions

• Contact: Emily.Lancsar@monash.edu

• Lancsar et al (2011) ‘Deriving Distributional Weights for QALYs through Discrete Choice Experiments’, Journal of Health Economics; 30: 466-478.

• Questions?