Don Husereau’s journal round-up for 25th November 2019

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.

Development and validation of the TRansparent Uncertainty ASsessmenT (TRUST) tool for assessing uncertainties in health economic decision models. PharmacoEconomics [PubMed] Published 11th November 2019

You’re going to quickly see that all three papers in today’s round-up align with some strong personal pet peeves that I harbour toward the nebulous world of market access and health technology assessment – most prominent is how loose we seem to be with language and form without overarching standards. This may be of no surprise to some when discussing a field which lacks a standard definition and for which many international standards of what constitutes good practice have never been defined.

This first paper deals with both issues and provides a useful tool for characterizing uncertainty. The authors state the purpose of the tool is “for systematically identifying, assessing, and reporting uncertainty in health economic models.” They suggest, to the best of their knowledge, no such tool exists. They also support the need for the tool by asserting that uncertainty in health economic modelling is often not fully characterized. The reasons, they suggest, are twofold: (1) there has been too much emphasis on imprecision; and (2) it is difficult to express all uncertainty.

I couldn’t agree more. What I sometimes deeply believe about those planning and conducting economic evaluation is that they obsess too often about uncertainty that is is less relevant (but more amenable to statistical adjustment) and don’t address uncertainty that payers actually care about. To wit, while it may be important to explore and adopt methods that deal with imprecision (dealing with known unknowns), such as improving utility variance estimates (from an SE of 0.003 to 0.011, yes sorry Kelvin and Feng for the callout), not getting this right is unlikely to lead to truly bad decisions. (Kelvin and Feng both know this.)

What is much more important for decision makers is uncertainty that stems from a lack of knowledge. These are unknown unknowns. In my experience this typically has to do with generalizability (how well will it work in different patients or against a different comparator?) and durability (how do I translate 16 weeks of data into a lifetime?); not things resolved by better variance estimates and probabilistic analysis. In Canada, our HTA body has even gone so far as to respond to the egregious act of not providing different parametric forms for extrapolation with the equally egregious act of using unrealistic time horizon adjustments to deal with this. Two wrongs don’t make a right.

To develop the tool, the authors first conducted a (presumably narrative) review of uncertainty frameworks and then ran identified concepts across a bunch of HTA expert committee types. They also used a previously developed framework as a basis for identifying all the places where uncertainty in HTA could occur. Using the concepts and the HTA areas they developed a tool which was presented a few times, and then validated through semi-structured interviews with different international stakeholders (N = 11), as well as insights into barriers to its use, user-friendliness, and feasibility.

Once the tool was developed, six case studies were worked up with an illustration of one of them (pembrolizumab for Hodgkin’s lymphoma) in the manuscript. While the tool does not provide a score or coefficient to adjust estimates or deal with uncertainty, it is not supposed to. What it is trying to do is make sure you are aware of them all so that you can make some determination as to whether the uncertainties are dealt with. One of the challenges of developing the tool is the lack of standardized terminology regarding uncertainty itself. While a short primer exists in the manuscript, for those who have looked into it, uncertainty terminology is far more uncertain than even the authors let on.

While I appreciate the tool and the attempt to standardize things, I do suspect the approach could have been strengthened (a systematic review and possibly a nominal group technique as is done for reporting guidelines). However, I’m not sure this would have gotten us much closer to the truth. Uncertainty needs to be sorted first and I am happy at their attempt. I hope it raises some awareness of how we can’t simply say we are “uncertain” as if that means something.

Unmet medical need: an introduction to definitions and stakeholder perceptions. Value in Health [PubMed] Published November 2019

The second, and also often-abused, term without an obvious definition is unmet medical need (UMN). My theory is that some confusion has arisen due to a confluence of marketing and clinical development teams and regulators. UMN has come to mean patients with rare diseases, drugs with ‘novel’ mechanisms of action, patients with highly prevalent disease, drugs with a more convenient formulation, or drugs with fewer side effects. And yet payers (in my experience) usually recognize none of these. Payers tend to characterize UMN in different ways: no drugs available to treat the condition, available drugs do not provide consistent or durable responses, and there have been no new medical developments in the area for > 10 years.

The purpose of this research then was to unpack the term UMN further. The authors conducted a comprehensive (gray) literature review to identify definitions of UMN in use by different stakeholders and then unpacked their meaning through definitions consultations with multi-European stakeholder discussions, trying to focus on the key elements of unmet medical need with a regulatory and reimbursement lens. This consisted of six one-hour teleconference calls and two workshops held in 2018. One open workshop involved 69 people from regulatory agencies, industry, payers, HTA bodies, patient organizations, healthcare, and academia.

A key finding of this work was that, yes indeed, UMN means different things to different people. A key dimension is whether unmet need is being defined in terms of individuals or populations. Population size (whether prevalent or rare) was not felt to be an element of the definition while there was general consensus that disease severity was. This means UMN should really only consider the UMNs of individual patients, not whether very few or very many patients are at need. It also means we see people who have higher rates of premature mortality and severe morbidity as having more of an unmet need, regardless of how many people are affected by the condition.

And last but not least was the final dimension of how many treatments are actually available. This, the authors point out, is the current legal definition in Europe (as laid down in Article 4, paragraph 2 of Commission Regulation [EC] No. 507/2006). And while this seems the most obvious definition of ‘need’ (we usually need things that are lacking) there was some acknowledgement by stakeholders that simply counting existing therapies is not adequate. There was also acknowledgement that there may be existing therapies available and still an UMN. Certainly this reflects my experience on the pan-Canadian Oncology Drug Review expert review committee, where unmet medical need was an explicit subdomain in their value framework, and where on more than one occasion it was felt, to my surprise, there was an unmet need despite the availability of two or more treatments.

Like the previous paper, the authors did not conduct a systematic review and could have consulted more broadly (no clinician stakeholders were consulted) or used more objective methods, a limitation they acknowledge but also unlikely to get them much further ahead in understanding. So what to do with this information? Well, the authors do propose an HTA approach that would triage reimbursement decision based on UMN. However, stakeholders commented that the method you use really depends on the HTA context. As such, the authors conclude that “the application of the definition within a broader framework depends on the scope of the stakeholder.” In other words, HTA must be fit for purpose (something we knew already). However, like uncertainty, I’m happy someone is actually trying to create reasonable coherent definitions of such an important concept.

On value frameworks and opportunity costs in health technology assessment. International Journal of Technology Assessment in Health Care [PubMed] Published 18th September 2019

The final, and most-abused term is that of ‘value’. While value seems an obvious prerequisite to those making investments in healthcare, and that we (some of us) are willing to acknowledge that value is what we are willing to give up to get something, what is less clear is what we want to get and what we want to give up.

The author of this paper, then, hopes to remind us of the various schools of thought on defining value in health that speak to these trade-offs. The first is broadly consistent with the welfarist school of economics and proposes that the value of health care used by decision makers should reflect individuals’ willingness to pay for it. An alternative approach – sometimes referred to as the extra-welfarist framework, argues that the value of a health technology should be consistent with the policy objectives of the health care system, typically health (the author states it is ‘health’ but I’m not sure it has to be). The final school of thought (which I was not familiar with and neither might you be which is the point of the paper) is what he terms ‘classical’, where the point is not to maximize a maximand or be held up to notions of efficiency but rather to discuss how consumers will be affected. The reference cited to support this framework is this interesting piece although I couldn’t find any allusion to the framework within.

What follows is a relatively fair treatment of extra-welfarist and welfarist applications to decision-making with a larger critical swipe at the former (using legitimate arguments that have been previously published – yes, extra-welfarists assume resources are divisible and, yes, extra-welfarists don’t identify the health-producing resources that will actually be displaced and, yes, using thresholds doesn’t always maximize health) and much downplay of the latter (how we might measure trade-offs reliably under a welfarist framework appears to be a mere detail until this concession is finally mentioned: “On account of the measurement issues surrounding [willingness to pay], there may be many situations in which no valid and reliable methods of operationalizing [welfarist economic value frameworks] exist.”) Given the premise of this commentary is that a recent commentary by Culyer seemed to overlook concepts of value beyond extra-welfarist ones, the swipe at extra-welfarist views is understandable. Hence, this paper can be seen as a kind of rebuttal and reminder that other views should not be ignored.

I like the central premise of the paper as summarized here:

“Although the concise term “value for money” may be much easier to sell to HTA decision makers than, for example, “estimated mean valuation of estimated change in mean health status divided by the estimated change in mean health-care costs,” the former loses too much in precision; it seems much less honest. Because loose language could result in dire consequences of economic evaluation being oversold to the HTA community, it should be avoided at all costs”

However, while I am really sympathetic to warning against conceptual shortcuts and loose language, I wonder if this paper misses the bigger point. Firstly, I’m not convinced we are making such bad decisions as those who wish the lambda to be silenced tend to want us to believe. But more importantly, while it is easy to be critical about economics applied loosely or misapplied, this paper (like others) offers no real practical solutions other than the need to acknowledge other frameworks. It is silent on the real reason extra-welfarist approaches and thresholds seem to have stuck around, namely, they have provided a practical and meaningful way forward for difficult decision-making and the HTA processes that support them. They make sense to decision-makers who are willing to overlook some of the conceptual wrinkles. And I’m a firm believer that conceptual models are a starting point for pragmatism. We shouldn’t be slaves to them.

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Rita Faria’s journal round-up for 13th May 2019

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.

Communicating uncertainty about facts, numbers and science. Royal Society Open Science Published 8th May 2019

This remarkable paper by Anne Marthe van der Bles and colleagues, including the illustrious David Spiegelhalter, covers two of my most favourite topics: communication and uncertainty. They focused on epistemic uncertainty. That is, the uncertainty about facts, numbers and science due to limited knowledge (rather than due to the randomness of the world). This is what we could know more about, if we spent more resources in finding it out.

The authors propose a framework for communicating uncertainty and apply it to two case studies, one in climate change and the other in economic statistics. They also review the literature on the effect of communicating uncertainty. It is so wide-ranging and exhaustive that, if I have any criticism, its 42 pages are not conducive to a leisurely read.

I found the distinction between direct and indirect uncertainty fascinating and incredibly relevant to health economics. Direct uncertainty is about the precision of the evidence whilst indirect uncertainty is about its quality. For example, evidence based on a naïve comparison of patients in a Phase 2 trial with historical controls in another country (yup, this happens!).

So, how should we communicate the uncertainty in our findings? I’m afraid that this paper is not a practical guide but rather a brilliant ground clearing exercise on how to start thinking about this. Nevertheless Box 5 (p35) does give some good advice! I do hope this paper kick-starts research on how to explain uncertainty beyond an academic audience. Looking forward to more!

Was Brexit triggered by the old and unhappy? Or by financial feelings? Journal of Economic Behavior & Organization [RePEc] Published 18th April 2019

Not strictly health economics – although arguably Brexit affects our health – is this impressive study about the factors that contributed to the Leave win in the Brexit referendum. Federica Liberini and colleagues used data from the Understanding Society survey to look at the predictors of people’s views about whether or not the UK should leave the EU. The main results are from a regression on whether or not a person was pro-Brexit, regressed on life satisfaction, their feelings on their financial situation, and other characteristics.

Their conclusions are staggering. They found that people’s views were generally unrelated to their age, their life satisfaction or their income. Instead, it was a person’s feelings about their financial situation that was the strongest predictor. For economists, it may be a bit cringe-worthy to see OLS used for a categorical dependent variable. But to be fair, the authors mention that the results are similar with non-linear models and they report extensive supplementary analyses. Remarkably, they’re making the individual level data available on the 18th of June here.

As the authors discuss, it is not clear if we’re looking at predictive estimates of characteristics related to pro-Brexit feeling or at causal estimates of factors that led to the pro-Brexit feeling. That is, if we could improve someone’s perceived financial situation, would we reduce their probability of feeling pro-Brexit? In any case, the message is clear. Feelings matter!

How does treating chronic hepatitis C affect individuals in need of organ transplants in the United Kingdom? Value in Health Published 8th March 2019

Anupam Bapu Jena and colleagues looked at the spillover benefits of curing hepatitis C given its consequences on the supply and demand of liver and other organs for transplant in the UK. They compare three policies: the status quo, in which there is no screening for hepatitis C and organ donation by people with hepatitis C is rare; universal screen and treat policy where cured people opt-in for organ donation; and similarly, but with opt-out for organ donation.

To do this, they adapted a previously developed queuing model. For the status quo, the model inputs were estimated by calibrating the model outputs to reported NHS performance. They then changed the model inputs to reflect the anticipated impact of the new policies. Importantly, they assumed that all patients with hepatitis C would be cured and no longer require a transplanted organ; conversely, that cured patients would donate organs at similar rates to the general population. They predict that curing hepatitis C would directly reduce the waiting list for organ transplants by reducing the number of patients needing them. Also, there would be an indirect benefit via increasing their availability to other patients. These consequences aren’t typically included in the cost-effectiveness analysis of treatments for hepatitis C, which means that their comparative benefits and costs may not be accurate.

Keeping in the theme of uncertainty, it was disappointing that the paper does not include some sort of confidence bounds on its results nor does it present sensitivity analysis to their assumptions, which in my view, were quite favourable towards a universal screen and test policy. This is an interesting application of a queuing model, which is something I don’t often see in cost-effectiveness analysis. It is also timely and relevant, given the recent drive by the NHS to eliminate hepatitis C. In a few years’ time, we’ll hopefully know to what extent the predicted spillover benefits were realised.

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James Altunkaya’s journal round-up for 3rd 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.

Sensitivity analysis for not-at-random missing data in trial-based cost-effectiveness analysis: a tutorial. PharmacoEconomics [PubMed] [RePEc] Published 20th April 2018

Last month, we highlighted a Bayesian framework for imputing missing data in economic evaluation. The paper dealt with the issue of departure from the ‘Missing at Random’ (MAR) assumption by using a Bayesian approach to specify a plausible missingness model from the results of expert elicitation. This was used to estimate a prior distribution for the unobserved terms in the outcomes model.

For those less comfortable with Bayesian estimation, this month we highlight a tutorial paper from the same authors, outlining an approach to recognise the impact of plausible departures from ‘Missingness at Random’ assumptions on cost-effectiveness results. Given poor adherence to current recommendations for the best practice in handling and reporting missing data, an incremental approach to improving missing data methods in health research may be more realistic. The authors supply accompanying Stata code.

The paper investigates the importance of assuming a degree of ‘informative’ missingness (i.e. ‘Missingness not at Random’) in sensitivity analyses. In a case study, the authors present a range of scenarios which assume a decrement of 5-10% in the quality of life of patients with missing health outcomes, compared to multiple imputation estimates based on observed characteristics under standard ‘Missing at Random’ assumptions. This represents an assumption that, controlling for all observed characteristics used in multiple imputation, those with complete quality of life profiles may have higher quality of life than those with incomplete surveys.

Quality of life decrements were implemented in the control and treatment arm separately, and then jointly, in six scenarios. This aimed to demonstrate the sensitivity of cost-effectiveness judgements to the possibility of a different missingness mechanism in each arm. The authors similarly investigate sensitivity to higher health costs in those with missing data than predicted based on observed characteristics in imputation under ‘Missingness at Random’. Finally, sensitivity to a simultaneous departure from ‘Missingness at Random’ in both health outcomes and health costs is investigated.

The proposed sensitivity analyses provide a useful heuristic to assess what degree of difference between missing and non-missing subjects on unobserved characteristics would be necessary to change cost-effectiveness decisions. The authors admit this framework could appear relatively crude to those comfortable with more advanced missing data approaches such as those outlined in last month’s round-up. However, this approach should appeal to those interested in presenting the magnitude of uncertainty introduced by missing data assumptions, in a way that is easily interpretable to decision makers.

The impact of waiting for intervention on costs and effectiveness: the case of transcatheter aortic valve replacement. The European Journal of Health Economics [PubMed] [RePEc] Published September 2018

This paper appears in print this month and sparked interest as one of comparatively few studies on the cost-effectiveness of waiting lists. Given interest in using constrained optimisation methods in health outcomes research, highlighted in this month’s editorial in Value in Health, there is rightly interest in extending the traditional sphere of economic evaluation from drugs and devices to understanding the trade-offs of investing in a wider range of policy interventions, using a common metric of costs and QALYs. Rachel Meacock’s paper earlier this year did a great job at outlining some of the challenges involved broadening the scope of economic evaluation to more general decisions in health service delivery.

The authors set out to understand the cost-effectiveness of delaying a cardiac treatment (TVAR) using a waiting list of up to 12 months compared to a policy of immediate treatment. The effectiveness of treatment at 3, 6, 9 & 12 months after initial diagnosis, health decrements during waiting, and corresponding health costs during wait time and post-treatment were derived from a small observational study. As treatment is studied in an elderly population, a non-ignorable proportion of patients die whilst waiting for surgery. This translates to lower modelled costs, but also lower quality life years in modelled cohorts where there was any delay from a policy of immediate treatment. The authors conclude that eliminating all waiting time for TVAR would produce population health at a rate of ~€12,500 per QALY gained.

However, based on the modelling presented, the authors lack the ability to make cost-effectiveness judgements of this sort. Waiting lists exist for a reason, chiefly a lack of clinical capacity to treat patients immediately. In taking a decision to treat patients immediately in one disease area, we therefore need some judgement as to whether the health displaced in now untreated patients in another disease area is of greater, less or equal magnitude to that gained by treating TVAR patients immediately. Alternately, modelling should include the cost of acquiring additional clinical capacity (such as theatre space) to treat TVAR patients immediately, so as not to displace other treatments. In such a case, the ICER is likely to be much higher, due to the large cost of new resources needed to reduce waiting times to zero.

Given the data available, a simple improvement to the paper would be to reflect current waiting times (already gathered from observational study) as the ‘standard of care’ arm. As such, the estimated change in quality of life and healthcare resource cost from reducing waiting times to zero from levels observed in current practice could be calculated. This could then be used to calculate the maximum acceptable cost of acquiring additional treatment resources needed to treat patients with no waiting time, given current national willingness-to-pay thresholds.

Admittedly, there remain problems in using the authors’ chosen observational dataset to calculate quality of life and cost outcomes for patients treated at different time periods. Waiting times were prioritised in this ‘real world’ observational study, based on clinical assessment of patients’ treatment need. Thus it is expected that the quality of life lost during a waiting period would be lower for patients treated in the observational study at 12 months, compared to the expected quality of life loss of waiting for the group of patients judged to need immediate treatment. A previous study in cardiac care took on the more manageable task of investigating the cost-effectiveness of different prioritisation strategies for the waiting list, investigating the sensitivity of conclusions to varying a fixed maximum wait-time for the last patient treated.

This study therefore demonstrates some of the difficulties in attempting to make cost-effectiveness judgements about waiting time policy. Given that the cost-effectiveness of reducing waiting times in different disease areas is expected to vary, based on relative importance of waiting for treatment on short and long-term health outcomes and costs, this remains an interesting area for economic evaluation to explore. In the context of the current focus on constrained optimisation techniques across different areas in healthcare (see ISPOR task force), it is likely that extending economic evaluation to evaluate a broader range of decision problems on a common scale will become increasingly important in future.

Understanding and identifying key issues with the involvement of clinicians in the development of decision-analytic model structures: a qualitative study. PharmacoEconomics [PubMed] Published 17th August 2018

This paper gathers evidence from interviews with clinicians and modellers, with the aim to improve the nature of the working relationship between the two fields during model development.

Researchers gathered opinion from a variety of settings, including industry. The main report focusses on evidence from two case studies – one tracking the working relationship between modellers and a single clinical advisor at a UK university, with the second gathering evidence from a UK policy institute – where modellers worked with up to 11 clinical experts per meeting.

Some of the authors’ conclusions are not particularly surprising. Modellers reported difficulty in recruiting clinicians to advise on model structures, and further difficulty in then engaging recruited clinicians to provide relevant advice for the model building process. Specific comments suggested difficulty for some clinical advisors in identifying representative patient experiences, instead diverting modellers’ attention towards rare outlier events.

Study responses suggested currently only 1 or 2 clinicians were typically consulted during model development. The authors recommend involving a larger group of clinicians at this stage of the modelling process, with a more varied range of clinical experience (junior as well as senior clinicians, with some geographical variation). This is intended to help ensure clinical pathways modelled are generalizable. The experience of one clinical collaborator involved in the case study based at a UK university, compared to 11 clinicians at the policy institute studied, perhaps may also illustrate a general problem of inadequate compensation for clinical time within the university system. The authors also advocate the availability of some relevant training for clinicians in decision modelling to help enhance the efficiency of participants’ time during model building. Clinicians sampled were supportive of this view – citing the need for further guidance from modellers on the nature of their expected contribution.

This study ties into the general literature regarding structural uncertainty in decision analytic models. In advocating the early contribution of a larger, more diverse group of clinicians in model development, the authors advocate a degree of alignment between clinical involvement during model structuring, and guidelines for eliciting parameter estimates from clinical experts. Similar problems, however, remain for both fields, in recruiting clinical experts from sufficiently diverse backgrounds to provide a valid sample.

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