OHE Lunchtime Seminar: Early Experience with HTA of Gene Therapies in the US: Pricing and Paying for Cures

The time is rapidly approaching when health care funders and health care systems around the world must be able to manage a growing number of drugs offering potential cures for a wide range of illnesses. The innovation pipeline includes potential cures for haemophilia, sickle cell disease, and numerous other genetically-based conditions. Individually, each treatment will be extremely expensive, and in the aggregate their costs are projected to have a substantial impact on the affordability of health care. Stated frankly, health systems today are ill prepared to face the challenge of assessing and paying for the potential cures that will soon become available.

As the leading health technology assessment organization in the US, ICER has completed reviews of three gene therapies and is in the midst of a fourth review.  Dr Steven Pearson, ICER’s President, will discuss the distinctive challenges in analysing the clinical and cost-effectiveness of these therapies, and the lessons learned to date. As part of this discussion Dr Pearson will present the early outlines of options for new methods to calculate “value-based” prices for potential cures that offer magnitudes of health gains and future cost offsets that have never been generated by traditional treatments.

Steven D. Pearson, MD, MSc is the Founder and President of the Institute for Clinical and Economic Review (ICER), an independent health technology assessment organization based in the US. Prominent among its evidence reports are ICER reviews of new drugs that include full assessments of clinical and cost-effectiveness along with suggested “value-based price benchmarks” to guide price and coverage negotiation. Dr Pearson is a Lecturer in the Department of Population Medicine at Harvard Medical School and also serves as Visiting Scientist in the Department of Bioethics at the National Institutes of Health. His published work includes over 100 articles on quality of care, the role of evidence-based medicine within the health care system, and related clinical, ethical, and organizational policy challenges. His book, No Margin, No Mission: Health Care Organizations and the Quest for Ethical Excellence, was published by Oxford University Press.

If you would like to attend this seminar, please reply to ohegeneral@ohe.org.

Click here to see the full invite.

Webinar facilities will also be available for this lunchtime seminar, however registration is needed. Please send an email to ohegeneral@ohe.org if you wish to join. Details of the webinar will be sent out closer to the event date.

Simon McNamara’s journal round-up for 1st October 2018

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

A review of NICE appraisals of pharmaceuticals 2000-2016 found variation in establishing comparative clinical effectiveness. Journal of Clinical Epidemiology [PubMed] Published 17th September 2018

The first paper in this week’s round-up is on the topic on single arm studies; specifically, the way in which the comparative effectiveness of medicines granted a marketing authorisation on the basis of single arm studies have been evaluated in NICE appraisals. If you are interested in comparative effectiveness, single arm studies are difficult to deal with. If you don’t have a control arm to refer to, how do you know what the impact of the intervention is? If you don’t know how effective the intervention is, how can you say whether it is cost-effective?

In this paper, the authors conduct a review into the way this problem has been dealt with during NICE appraisals. They do this by searching through the 489 NICE technology appraisals conducted between 2010 and 2016. The search identified 22 relevant appraisals (4% of the total). The most commonly used way of estimating comparative effectiveness (19 of 22 appraisals) was simulation of a control arm using external data – be that from observational study or a randomised trial. Of these,14 of the appraisals featured naïve comparison across studies, with no attempt made to adjust for potential differences between population groups. The three appraisals that didn’t use external data were reliant upon the use of expert opinion, or the assumption that non-responders in the intervention single-arm study could be used as a proxy for those who would receive the comparator intervention.

Interestingly, the authors find little difference between the proportion of medicines reliant on non-RCT data being approved by NICE (83%), compared to those with RCT data (86%), however; the likelihood of receiving an “optimised” (aka subgroup) approval was substantially higher for medicines with solely non-RCT data (41% vs 19%). These findings demonstrate that NICE do accept models based on single-arm studies – even if more than 75% of the comparative effectiveness estimates these models were based on were reliant upon naïve indirect comparisons, or other less robust methods.

The paper concludes by noting that single-arm studies are becoming more common (50% of the appraisals identified were conducted in 2015-2016), and suggesting that HTA and regulatory bodies should work together, to develop guidance on how to evaluate comparative effectiveness based on single-arm studies.

I thought this paper was great, and it made me reflect on a couple of things. Firstly, the fact that NICE completed such a high volume of appraisals (489) between 2010 and 2016 is extremely impressive – well done NICE. Secondly, should the EMA, or EUnetHTA, play a larger role in providing estimates of comparative effectiveness for single arm studies? Whilst different countries may reasonably make different value judgements about different health outcomes, comparative effectiveness is – at least in theory – a matter of fact, rather than values, so can’t we assess it centrally?

A QALY loss is a QALY loss is a QALY loss: a note on independence of loss aversion from health states. The European Journal of Health Economics [PubMed] Published 18th September 2018

If I told you that you would receive £10 in return for doing some work for me, and then I only paid you £5, how annoyed would you be? What about if I told you I would give you £10 but then gave you £15? How delighted would you be? If you are economically rational then these two impacts (annoyance vs being delighted) should be symmetrical; but, if you are a human, your annoyance in the first scenario would likely outweigh the delight you would experience in the second. This is the basic idea behind Kahneman and Tversky’s seminal work on “loss aversion” – we dislike changes we perceive as losses more than we like equivalent changes we perceive as gains. The second paper in this week’s roundup explores loss aversion in the context of health. Application of loss aversion in health is a really interesting idea, because it calls into question the idea that people value all QALYs equally – perhaps QALYs perceived as losses are valued more highly than QALYs perceived as gains.

In the introduction of this paper, the authors note that existing evidence suggests loss aversion is present for duration of life, and for quality of life, but note that nobody has explored whether loss aversion remains constant if the two elements change together – simply put, when it comes to loss aversion is “a QALY loss a QALY loss a QALY loss”? The authors test this idea via a choice experiment fielded in a sample of 111 Dutch students. In this experiment, the loss aversion of each participant was independently elicited for four EQ-5D-5L health states – ranging from perfect health down to a health state utility value of 0.46.

As you might have guessed from the title of the paper, the authors found that, at the aggregate level, loss aversion was not significantly different between the four health states – albeit with some variation at the individual level. For each health state, perceived losses were weighted around two times as highly as perceived gains.

I enjoyed this paper, and it prompted me to think about the consequences of loss-aversion for health economics more generally. Do health related decision makers treat the outcomes associated with a new technology as a reference-point, and so feel loss aversion when considering not funding it? From a normative perspective, should we accept asymmetry in the valuation of health? Is this simply a behavioural quirk that we should over-ride in our analyses, or should we be conforming to it and granting differential weight to outcomes depending upon whether the recipient perceives it as a gain or a loss?

Advanced therapy medicinal products and health technology assessment principles and practices for value-based and sustainable healthcare. The European Journal of Health Economics [PubMed] Published 18th September 2018

The final paper in this week’s roundup is on “Advanced Therapy Medicinal Products” (ATMPs). According to the European Union Regulation 1394/2007, an ATMP is a medicine which is either (1) a gene therapy, (2) a somatic-cell therapy, (3) a tissue-engineered therapy, or (4) a combination of these approaches. I don’t pretend to understand the nuances of how these medicines work, but in simple terms ATMPs aim to replace, or regenerate, human cells, tissues and organs in order to treat ill health. Whilst ATMPs are thought to have great potential in improving health and providing long-term survival gains, they present a number of challenges for Health Technology Assessment (HTA) bodies.

This paper details a meeting of a panel of experts from the UK, Germany, France and Sweden, who were tasked with identifying and discussing these challenges. The experts identified three key challenges; (1) uncertainty of long-term benefit, and subsequently cost-effectiveness, (2) discount rates, and (3) capturing the broader “value” of these therapies – including the incremental value associated with potentially curative therapies. These three challenges stem from the fact that at the point of HTA, ATMPs are likely to have immature data and the uncertain prospect of long-term benefits. The experts suggest a range of solutions to these problems, including the use of outcomes-based reimbursement schemes, initiating a multi-disciplinary forum to consider different approaches to discounting, and further research into elements of “value” not captured by current HTA processes.

Whilst there is undoubtedly merit to some of these suggestions, I couldn’t help but feel a bit uneasy about this paper due to its funder – an ATMP manufacturer. Would the authors have written this paper if they hadn’t been paid to by a company with a vested interest in changing HTA systems to suit their agenda? Whilst I don’t doubt the paper was written independently of the company, and don’t mean to cast aspersions on the authors, this does make me question how industry shapes the areas of discourse in our field – even if it doesn’t shape the specific details of that discourse.

Many of the problems raised in this paper are not unique to ATMPs, they apply equally to all interventions with the uncertain prospect of potential cure or long-term benefit (e.g. for therapies for the treatment of early stage cancer, public health interventions or immunotherapies). Science aside, funder aside, what makes ATMPs any different to these prior interventions?

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Chris Sampson’s journal round-up for 17th September 2018

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Does competition from private surgical centres improve public hospitals’ performance? Evidence from the English National Health Service. Journal of Public Economics Published 11th September 2018

This study looks at proper (supply-side) privatisation in the NHS. The subject is the government-backed introduction of Independent Sector Treatment Centres (ISTCs), which, in the name of profit, provide routine elective surgical procedures to NHS patients. ISTCs were directed to areas with high waiting times and began rolling out from 2003.

The authors take pre-surgery length of stay as a proxy for efficiency and hypothesise that the entry of ISTCs would improve efficiency in nearby NHS hospitals. They also hypothesise that the ISTCs would cream-skim healthier patients, leaving NHS hospitals to foot the bill for a more challenging casemix. Difference-in-difference regressions are used to test these hypotheses, the treatment group being those NHS hospitals close to ISTCs and the control being those not likely to be affected. The authors use patient-level Hospital Episode Statistics from 2002-2008 for elective hip and knee replacements.

The key difficulty here is that the trend in length of stay changed dramatically at the time ISTCs began to be introduced, regardless of whether a hospital was affected by their introduction. This is because there was a whole suite of policy and structural changes being implemented around this period, many targeting hospital efficiency. So we’re looking at comparing new trends, not comparing changes in existing levels or trends.

The authors’ hypotheses prove right. Pre-surgery length of stay fell in exposed hospitals by around 16%. The ISTCs engaged in risk selection, meaning that NHS hospitals were left with sicker patients. What’s more, the savings for NHS hospitals (from shorter pre-surgery length of stay) were more than undermined by an increase in post-surgery length of stay, which may have been due to the change in casemix.

I’m not sure how useful difference-in-difference is in this case. We don’t know what the trend would have been without the intervention because the pre-intervention trend provides no clues about it and, while the outcome is shown to be unrelated to selection into the intervention, we don’t know whether selection into the ISTC intervention was correlated with exposure to other policy changes. The authors do their best to quell these concerns about parallel trends and correlated policy shocks, and the results appear robust.

Broadly speaking, the study satisfies my prior view of for-profit providers as leeches on the NHS. Still, I’m left a bit unsure of the findings. The problem is, I don’t see the causal mechanism. Hospitals had the financial incentive to be efficient and achieve a budget surplus without competition from ISTCs. It’s hard (for me, at least) to see how reduced length of stay has anything to do with competition unless hospitals used it as a basis for getting more patients through the door, which, given that ISTCs were introduced in areas with high waiting times, the hospitals could have done anyway.

While the paper describes a smart and thorough analysis, the findings don’t tell us whether ISTCs are good or bad. Both the length of stay effect and the casemix effect are ambiguous with respect to patient outcomes. If only we had some PROMs to work with…

One method, many methodological choices: a structured review of discrete-choice experiments for health state valuation. PharmacoEconomics [PubMed] Published 8th September 2018

Discrete choice experiments (DCEs) are in vogue when it comes to health state valuation. But there is disagreement about how they should be conducted. Studies can differ in terms of the design of the choice task, the design of the experiment, and the analysis methods. The purpose of this study is to review what has been going on; how have studies differed and what could that mean for our use of the value sets that are estimated?

A search of PubMed for valuation studies using DCEs – including generic and condition-specific measures – turned up 1132 citations, of which 63 were ultimately included in the review. Data were extracted and quality assessed.

The ways in which the studies differed, and the ways in which they were similar, hint at what’s needed from future research. The majority of recent studies were conducted online. This could be problematic if we think self-selecting online panels aren’t representative. Most studies used five or six attributes to describe options and many included duration as an attribute. The methodological tweaks necessary to anchor at 0=dead were a key source of variation. Those using duration varied in terms of the number of levels presented and the range of duration (from 2 months to 50 years). Other studies adopted alternative strategies. In DCE design, there is a necessary trade-off between statistical efficiency and the difficulty of the task for respondents. A variety of methods have been employed to try and ease this difficulty, but there remains a lack of consensus on the best approach. An agreed criterion for this trade-off could facilitate consistency. Some of the consistency that does appear in the literature is due to conformity with EuroQol’s EQ-VT protocol.

Unfortunately, for casual users of DCE valuations, all of this means that we can’t just assume that a DCE is a DCE is a DCE. Understanding the methodological choices involved is important in the application of resultant value sets.

Trusting the results of model-based economic analyses: is there a pragmatic validation solution? PharmacoEconomics [PubMed] Published 6th September 2018

Decision models are almost never validated. This means that – save for a superficial assessment of their outputs – they are taken at good faith. That should be a worry. This article builds on the experience of the authors to outline why validation doesn’t take place and to try to identify solutions. This experience includes a pilot study in France, NICE Evidence Review Groups, and the perspective of a consulting company modeller.

There are a variety of reasons why validation is not conducted, but resource constraints are a big part of it. Neither HTA agencies, nor modellers themselves, have the time to conduct validation and verification exercises. The core of the authors’ proposed solution is to end the routine development of bespoke models. Models – or, at least, parts of models – need to be taken off the shelf. Thus, open source or otherwise transparent modelling standards are a prerequisite for this. The key idea is to create ‘standard’ or ‘reference’ models, which can be extensively validated and tweaked. The most radical aspect of this proposal is that they should be ‘freely available’.

But rather than offering a path to open source modelling, the authors offer recommendations for how we should conduct ourselves until open source modelling is realised. These include the adoption of a modular and incremental approach to modelling, combined with more transparent reporting. I agree; we need a shift in mindset. Yet, the barriers to open source models are – I believe – the same barriers that would prevent these recommendations from being realised. Modellers don’t have the time or the inclination to provide full and transparent reporting. There is no incentive for modellers to do so. The intellectual property value of models means that public release of incremental developments is not seen as a sensible thing to do. Thus, the authors’ recommendations appear to me to be dependent on open source modelling, rather than an interim solution while we wait for it. Nevertheless, this is the kind of innovative thinking that we need.

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