Rita Faria’s journal round-up for 22nd 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.

Economically efficient hepatitis C virus treatment prioritization improves health outcomes. Medical Decision Making [PubMed] Published 22th August 2018

Hepatitis C treatment was in the news a couple of years ago when the new direct-acting antivirals first appeared on the scene. These drugs are very effective but also incredibly expensive. This prompted a flurry of cost-effectiveness analyses and discussions of the role of affordability in cost-effectiveness (my views here).

This compelling study by Lauren Cipriano and colleagues joins the debate by comparing various strategies to prioritise patients for treatment when the budget is not enough to meet patient demand. This is a clear example of the health losses due to the opportunity cost.

The authors compare the costs and health outcomes of various prioritisation schedules in terms of the number of patients treated, the distribution by severity and age, time to treatment, impact on end-stage liver disease, QALYs, costs and net benefit.

The differences between prioritisation schedules in terms of these various outcomes were remarkable. Reassuringly, the optimal prioritisation schedule on the basis of net benefit (the “optimisation” schedule) was the one that achieved the most QALYs and the greatest net benefit. This was even though the cost-effectiveness threshold did not reflect the opportunity cost, as it was set at $100,000 per QALY gained.

This study is fascinating. It shows how the optimal policy depends on what we are trying to maximise. The “first come first serve” schedule treats the most patients, but it is the “optimisation” schedule that achieves the most health benefits net of the opportunity cost.

Since their purpose was not to compare treatments, the authors used a representative price depending on whether patients had progressed to cirrhosis. A future study could include a comparison between drugs, as our previous work found that there are clear differences in cost-effectiveness between treatment strategies. The more cost-effective the treatment strategies, the more patients can be treated with a given budget.

The authors made the Excel model available as supporting material, together with documentation. This is excellent practice! It disseminates the work and shows openness to independent validation. Well done!

Long-term survival and value of chimeric antigen receptor T-cell therapy for pediatric patients with relapsed or refractory leukemia. JAMA Pediatrics [PubMed] Published 8th October 2018

This fascinating study looks at the cost-effectiveness of tisagenlecleucel in the treatment of children with relapsed or refractory leukaemia compared to chemotherapy.

Tisagenlecleucel is the first chimeric antigen receptor T-cell (CAR-T) therapy. CAR-T therapy is the new kid on the block in cancer treatment. It involves modifying the patient’s own immune system cells to recognise and kill the patient’s cancer (see here for details). Such high-tech treatment comes with a hefty price tag. Tisagenlecleucel is listed at $475,000 for a one-off administration.

The key challenge was to obtain the effectiveness inputs under the chemotherapy option. This was because tisagenlecleucel has only been studied in single-arm trials and individual level data was not available to the research team. The research team selected a single-arm study on the outcomes with clofarabine monotherapy, since its patients at baseline were most similar in terms of demographics and number of prior therapies to the tisagenlecleucel study.

This study is brilliant in approaching a difficult decision problem and conducting extensive sensitivity analysis. In particular, it tests the impact of common drivers of the cost-effectiveness of potentially curative therapies in children, such as the discount rate, duration of benefit, treatment initiation, and the inclusion of future health care costs. Ideally, the sensitivity analysis should also have tested the assumption that the studies informing the effectiveness inputs for tisagenlecleucel and clofarabine monotherapy were comparable or if clofarabine monotherapy does not represent the current standard of care, although it would be difficult to parameterise.

This outstanding study highlights the challenges posed by the approval of treatments based on single-arm studies. Had individual-level data been available, an adjusted comparison may have been possible, which would improve the degree of confidence in the cost-effectiveness of tisagenlecleucel. Regulators and trial sponsors should work together to make anonymised individual level data available to bonafide researchers.

Researcher requests for inappropriate analysis and reporting: a U.S. survey of consulting biostatisticians. Annals of Internal Medicine [PubMed] Published 10th October 2018

This study reports a survey of biostatisticians on the frequency and severity of requests for inappropriate analysis and reporting. The results are stunning!

The top 3 requests in terms of severity were to falsify statistical significance to support a desired result, change data to achieve the desired outcome and remove/alter data records to better support the research hypothesis. Fortunately, this sort of requests appears to be rare.

The top 3 requests in terms of frequency seem to be not showing a plot because it does not show an effect as strong as it had been hoped; to stress only the significant findings but under-reporting non-significant ones, and report results before data have been cleaned and validated.

Given the frequency and severity of the requests, the authors recommend that researchers should be better educated in good statistical practice and research ethics. I couldn’t agree more and would suggest that cost-effectiveness analysis is included, given that it informs policy decisions and it is generally conducted by multidisciplinary teams.

I’m now wondering what the responses would be if we did a similar survey to health economists, particularly those working in health technology assessment! Something for HESG, iHEA or ISPOR to look at for the future?

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On the commensurability of efficiency

In this week’s round-up, I highlighted a recent paper in the journal Cambridge Quarterly of Healthcare Ethics. There are some interesting ideas presented regarding the challenge of decision-making at the individual patient level, and in particular a supposed trade-off between achieving efficiency and satisfying health need.

The gist of the argument is that these two ‘values’ are incommensurable in the sense that the comparative value of two choices is ambiguous where the achievement of efficiency and need satisfaction needs to be traded. In the journal round-up, I highlighted 2 criticisms. First, I suggested that efficiency and health need satisfaction are commensurable. Second, I suggested that the paper did not adequately tackle the special nature of microlevel decision-making. The author – Anders Herlitz – was gracious enough to respond to my comments with several tweets.

Here, I’d like to put forth my reasoning on the subject (albeit with an ignorance of the background literature on incommensurability and other matters of ethics).

Consider a machine gun

A machine gun is far more efficient than a pistol, right? Well, maybe. A machine gun can shoot more bullets than a pistol over a sustained period. Likewise, a doctor who can treat 50 patients per day is more efficient than a doctor who can treat 20 patients per day.

However, the premise of this entire discussion, as established by Herlitz, is values. Herlitz introduces efficiency as a value and not as some dispassionate indicator of return on input. When we are considering values – as we necessarily are when we are discussing decision-making and more generally ‘what matters’ – we cannot take the ‘more bullets’ approach to assessing efficiency.

That’s because ‘more bullets’ is not what we mean when we talk about the value of efficiency. The production function is fundamental to our understanding of efficiency as a value. Once values are introduced, it is plain to see that in the context of war (where value is attached to a greater number of deaths) a machine gun may very well be considered more efficient. However, bearing a machine gun is far less efficient than bearing a pistol in a civilian context because we value a situation that results in fewer deaths.

In this analogy, bullets are health care and deaths are (somewhat confusingly, I admit) health improvement. Treating more people is not better because we want to provide more health care, but because we want to improve people’s health (along with some other basket of values).

Efficiency only has value with respect to the outcome in whose terms it is defined, and is therefore always commensurable with that outcome. That is, the production function is an inherent and necessary component of an efficiency to which we attach value.

I believe that Herlitz’s idea of incommensurability could be a useful one. Different outcomes may well be incommensurable in the way described in the paper. But efficiency has no place in this discussion. The incommensurability Herlitz describes in his paper seems to be a simple conflict between utilitarianism and prioritarianism, though I don’t have the wherewithal to pursue that argument so I’ll leave it there!

Microlevel efficiency trade-offs

Having said all that, I do think there could be a special decision-making challenge regarding efficiency at the microlevel. And that might partly explain Herlitz’s suggestion that efficiency is incommensurable with other outcomes.

There could be an incommensurability between values that can be measured in their achievement at the individual level (e.g. health improvement) and values that aren’t measured with individual-level outcomes (e.g. prioritisation of more severe patients). Those two outcomes are incommensurable in the way Herlitz described, but the simple fact that we tend to think about the former as an efficiency argument and the latter as an equity argument is irrelevant. We could think about both in efficiency terms (for example, treating n patients of severity x is more efficient than treating n-1 patients of severity x, or n patients of severity x-1), we just don’t. The difficulty is that this equity argument is meaningless at the individual level because it relies on information about outcomes outside the microlevel. The real challenge at the microlevel, therefore, is to acknowledge scope for efficiency in all outcomes of value. The incommensurability that matters is between microlevel and higher-level assessments of value.

As an aside, I was surprised that the Rule of Rescue did not get a mention in the paper. This is a perfect example of a situation in which arguments that tend to be made on efficiency grounds are thrown out and another value (the duty to save an immediately endangered life) takes over. One doesn’t need to think very hard about how Rule of Rescue decision-making could be framed as efficient.

In short, efficiency is never incommensurable because it is never an end in itself. If you’re concerned with being more efficient for the sake of being more efficient then you are probably not making very efficient decisions.

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Chris Sampson’s journal round-up for 18th December 2017

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.

Individualized glycemic control for U.S. adults with type 2 diabetes: a cost-effectiveness analysis. Annals of Internal Medicine [PubMed] Published 12th December 2017

The nature of diabetes – that it affects a lot of people and is associated with a wide array of physiological characteristics and health impacts – has given rise to recommendations for individualisation of care. This paper evaluates individualisation of glycemic control targets. Specifically, the individualised programme allocated people to one of 3 HbA1c targets (<6.5%, <7%, <8%) according to their characteristics, while the comparator was based on a single fixed target (<7%). The researchers used a patient-level simulation model. Risk equations developed by the UKPDS study were used to predict diabetes complications and mortality. The baseline population was derived from the NHANES study for 2011-12 and constitutes people who self-reported as having diabetes and who were at least 30 years old at diagnosis (to try and isolate type 2 diabetes). It’s not much of a surprise that the individualised approach dominated uniform intensive control, saving $13,547 on average per patient with a slight improvement in QALY outcomes. But the findings are not all in favour of individualisation. Quality of life improvements due to the benefits of medication were partially counteracted by a slight decrease in life years gained due to a higher rate of (mortality-increasing) complications. The absolute lifetime risk of myocardial infarction was 1.39% higher with individualisation. A key outstanding question is how much the individualisation process would actually cost to get right. Granted, it probably wouldn’t cost as much as the savings estimated in this study, but the difficulty of ensuring adequate data quality to consistently inform individualisation should not be underestimated.

Microlevel prioritizations and incommensurability. Cambridge Quarterly of Healthcare Ethics [PubMed] Published 7th December 2017

This article concerns the ethical challenges of decision-making at the microlevel. For example, decisions may need to be made about allocating resources between 2 or more identifiable patients, perhaps within a particular clinic or amongst an individual clinician’s patients. The author asserts two relevant values: health need satisfaction and efficiency. Health need satisfaction is defined in terms of severity (regardless of capacity to benefit from available treatments), while efficiency is defined in terms of the maximisation of health benefit (subject to the effectiveness of treatment). The author then argues that these two values are incommensurable in the sense that we can have situations in which health need satisfaction is greater (or less) for a given choice over another, while efficiency could be lower (or higher). Thus, it is not always possible to rank choices given two non-cardinally-comparable values. It might not be clear whether it is better to treat patient A or patient B if the implications of doing so are different in terms of need and efficiency. The author then goes on to suggest some solutions to this apparent problem, starting by highlighting the need for decision makers (in this case clinicians) to recognise different decision paths. The first solution is to generate some guidelines that offer complete ordering of possible choices. These might be based on a process of weighting the different values (e.g. health need satisfaction and efficiency). The other ‘solution’ is to leave the decision to medical practitioners, who can create reasons for choices that may be unique to the case at hand. In this case, certain decision paths should be avoided, such as those that would entail discrimination. I have a lot of problems with this assessment of decision-making at the individual level. Mainly, the discussion is undermined by the fact that efficiency and health need satisfaction are entirely commensurable insofar as we care about either of them in relation to prioritisation in health care. We tend to understand both health need satisfaction and opportunity cost (the basis for estimating efficiency) in terms of health outcomes. The essay also fails to clearly identify the uniqueness of the challenge of microlevel decision-making as distinct from the process of creating clinical guidelines. This may call for a follow-up blog post…

EQ-5D: moving from three levels to five. Value in Health Published 6th December 2017

If you work on economic evaluation, the move from using the EQ-5D-3L to the EQ-5D-5L – in terms of the impact on our results – is one of the biggest methodological step changes in recent memory. We all know that the 5L (and associated value set for England) is better than the 3L. Don’t we? So it is perhaps a bit disappointing that the step to the 5L has been so tentative. This editorial articulates the challenge. NICE makes standards. EuroQoL does research. NICE was (relatively) satisfied with the 3L. EuroQoL wasn’t. We have a clash between an inherently (perhaps necessarily) conservative institution and an inherently progressive institution. Hopefully, their interaction will put us on a sustainable path that achieves both methodological consistency and scientific rigour. This editorial also provides us with a DOI-citable account of the saga that includes the development of the 5L value set for England and NICE’s subsequent memorandum.

Current UK practices on health economics analysis plans (HEAPs): are we using heaps of them? PharmacoEconomics [PubMed] Published 6th December 2017

You could get by for years in economic evaluation without even hearing about ‘health economics analysis plans’ (HEAPs). It probably depends on the policies set by the clinical trials unit (CTU) that you’re working with. The idea is that HEAPs are an equivalent standard operating procedure (SOP) to a statistical analysis plan – setting out how the trial data will be analysed before the analysis begins. This could aid transparency and consistency, and prevent dodgy practices. In this study, the researchers sought to find out whether HEAPs are actually being used, and their perceived role in clinical trials research. A survey targeted 46 UK CTUs, asking about the role of health economists in the unit and whether they used HEAP SOPs. Of 28 respondents, 11 reported having an embedded health economics team. A third of CTUs reported always having a HEAP in place. Most said they only used HEAPs ‘sometimes’, and publicly funded trials were said to be more likely to use a HEAP. The majority of respondents agreed it was acceptable to produce the HEAP at any point prior to a lockdown of the data. The findings demonstrate inconsistency in who writes HEAPs and who is perceived to be the audience. I agree with the premise that we need HEAPs. Though I’m not sure what they should look like, except that statistical analysis plans probably should not be used as a template. It would be good if some of these researchers took things a step further and figured out what ought to go into a HEAP, so that we can consistently employ their recommendations. If you’re on the HEALTHECON-ALL mailing list, you’ll know that they’re already on the case.

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