ISPOR 2020 – inside the third plenary: leaving the QALY behind…but not completely
How can traditional cost–effectiveness analyses, models and measures be adapted to generate more valuable insights about what patients actually value? Here, I share some of my takeaways from the third – and final – plenary session at Virtual ISPOR 2020 (18–20 May).
Due to the COVID-19 outbreak, the 2020 ISPOR Annual Meeting, due to be held in Orlando (FL, USA) over 18–20 May 2020, is instead taking place virtually over these same dates!
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The third plenary session of the virtual event was moderated by Deborah Freund (Claremont Graduate University, CA, USA) and featured Anirban Basu (University of Washington, WA, USA), Susan Griffin (University of York, UK) and Darius Lakdawalla (University of Southern California, Los Angeles, CA, USA) as panelists.
What novel estimates of value do traditional cost–effectiveness analyses often fail to capture? How may cost–effectiveness analyses be easily adapted into distributional cost–effectiveness that can help inform on the impact of a medical technology on health inequality as well as overall population health? What effectiveness measures exist besides quality-adjusted life years (QALYs)? Here, I share some of my takeaways from the meeting’s third virtual plenary!
The value of hope
In the first presentation of the session, Lakdawalla explained how, in many cases, traditional cost–effectiveness analyses can struggle to represent and match the extent of value placed by patients on particular novel therapies, especially those for rare diseases or cancers. Indeed, this was a point Marc Boutin (National Health Council, DC, USA) raised during the meeting's second virtual plenary as well.
Lakdawalla described some of the work the ISPOR Value Assessment Task Force has been performing, generating a ‘value flower’ – with ‘petals’ representing a wide range of aspects to value, beyond simply the traditional ones most commonly considered such as cost and QALYs. Some of these novel value elements include ‘insurance’ value – i.e. the idea that individuals are willing to pay a certain amount to avoid becoming sick or to ensure if they are unwell, they have access to care – and the concept of the ‘value of hope’.
The value of hope is a particularly interesting concept; Lakdawalla described research that demonstrates that patients, even risk-averse individuals, are often willing to display preference for a riskier treatment strategy compared with one that offers, for example, a greater mean overall survival rate. If a treatment has a lower associated mean over survival gain, but carries with it a greater likelihood of patients living longer than that mean, patients may show preference for that treatment.
Lakdawalla explained that many in the field are reticent to adopt this expanded ‘value flower’; there is worry that many of the ‘petals’ of the flower overlap, which may lead to double counting and overestimation of a products value.
There is a clear need for a unified assessment framework to be developed that incorporates all novel and traditional elements of value, with microeconomic model foundations.
But how do we go about building this framework?
Risk is an element of value
Lakdawalla explained that to build a unified value assessment framework, it is essential to acknowledge that risk is an element of various novel elements of value, including insurance value and value of hope. This risk needs to be incorporated into the microeconomic models supporting the framework.
Evaluating a new medical technology utilizing such an ‘expanded’ value framework, value is determined according to traditional value-based QALYs, insurance value and value of hope. A risk-adjusted QALY that can be used in decisions analysis in the same way as traditional QALYs is generated, with one important difference – it accounts for risk!
In line with, as mentioned, the fact that patients often value treatments for more severe illnesses more, this risk-inclusive value assessment demonstrates that cost–effectiveness thresholds for treatments or new technologies for this disease should be higher.
Everyone deserves an equal chance of good health
Griffin gave the second presentation of the session, kicking off by emphasizing that it is a widely held view that everyone deserves an equal chance of good health, regardless of their socioeconomic status or other pre-disposing factors.
However, Griffin continued, we know in reality that disparities in healthcare are often wider than can be explained simply by biology or genetics alone; these disparities are systematic, plausibly avoidable difference that adversely affect disadvantaged groups. Further, the choices we make in healthcare can actually widen these disparities.
Fittingly, many sets of criteria for healthcare resource allocation put great emphasis on equity, fairness and minimizing disparity, compared with increasing overall population health alone, although this is also a key component. So, how can we balance these two essential components in health technology assessment to ensure decisions benefit not only the general population, but also tackle inequality?
Plenary 3 Susan Griffin @UniOfYork - impact of health #inequalities should be a component in our #assessment of #value for money, we need to describe the impact on overall #health and the #disparities as well as facilitate discussion on the trade-offs between the 2 #ISPORAnnual— ISPOR (@ISPORorg) May 20, 2020
QALYs, Griffin explained, were designed to help assess the ability of a technology to improve the general health of a population; they do not tell the whole story.
To balance these two elements in health technology assessment, distributional cost–effectiveness analyses may be key. These adapted cost–effectiveness analyses allow one to not only estimate the costs and outcomes associated with a new technology in general, but also estimate these in equity-relevant population subgroups – i.e. those of different socioeconomic statuses.
Though assessing inequality is challenging in and of itself, this type of distributional cost–effectiveness analysis represents progress in informing health technology assessment and resultant decision making based on effects on both the general population’s health and health inequality.
Health years in total – an alternative to QALYs?
The final presentation of the session was made by Basu, in which he discussed research he was involved in, Published in Value in Health, concerning a novel framework for valuing health outcomes in cost–effectiveness analyses, using health years in total (HYT) as a new effectiveness measure in these analyses, instead of QALYs.
A key issue associated with QALYs, Basu explained, is that, in their calculation, they account for individuals baseline health status. This means that calculated incremental QALY gains associated with a new technology are always valued more for individuals who display greater baseline health compared with other individuals.
The HYT effectiveness measure overcomes this limitation of the QALY, as well as other limitations associated with effectiveness measures such as the EVL.
Though there are limitations to HYTs too, the new measure can complement distributional cost–effectiveness analyses to help better inform health technology assessments and decision making.
So, are we abandoning QALYs in health technology assessment? No, not completely; QALYs are valuable measures and their inherent simplicity make them valuable for various necessary assessments. However, it is clear there is still progress to be made and, from the presentations in this session, with a potential novel value framework, distributional cost–effectiveness analyses and HYTs, we are certainly making progress!