Presentation on theme: "Deriving distributional weights for Quality Adjusted Life Years (QALYs) through discrete choice experiments Emily Lancsar Associate Professor Centre for."— Presentation transcript:
Deriving distributional weights for Quality Adjusted Life Years (QALYs) through discrete choice experiments Emily Lancsar Associate Professor Centre for Health Economics Monash University New Directions in Welfare 7 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 Bateman 1, Cam Donaldson 2, Michael Jones- Lee 2, Emily Lancsar 2, Graham Loomes 1, Helen Mason 2, Jose Luis Pinto Prades 1 Angela Robinson 1, Mandy Ryan 3, Phil Shackley 2, Richard Smith 1, Kerry Sproston 4, Robert Sugden 1, Heather Wardle 4, John Wildman 2 1.University of East Anglia 2.Newcastle University 3.University of Aberdeen 4.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 Design Description of attribute Levels Age at onset (years) 1 10 20 40 60 70 Age at death if untreated (yrs)1 10 20 40 60 70 80 Severity: Qol if untreated (%)0 30 60 90 Gain in Qol with treatment (%)0 10 20 40 70 100 Gain 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
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 weights 1.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
Deriving distributional weights CV values used to calculate weights: CV: number of QALYs required to equalise expected utility in the reference & alternative case QALY base : 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
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?