Chris Sampson’s journal round-up for 19th November 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.

Valuation of health states considered to be worse than death—an analysis of composite time trade-off data from 5 EQ-5D-5L valuation studies. Value in Health Published 12th November 2018

I have a problem with the idea of health states being ‘worse than dead’, and I’ve banged on about it on this blog. Happily, this new article provides an opportunity for me to continue my campaign. Health state valuation methods estimate how much a person prefers being in a more healthy state. Positive values are easy to understand; 1.0 is twice as good as 0.5. But how about the negative values? Is -1.0 twice as bad as -0.5? How much worse than being dead is that? The purpose of this study is to evaluate whether or not negative EQ-5D-5L values meaningfully discriminate between different health states.

The study uses data from EQ-5D-5L valuation studies conducted in Singapore, the Netherlands, China, Thailand, and Canada. Altogether, more than 5000 people provided valuations of 10 states each. As a simple measure of severity, the authors summed the number of steps from full health in all domains, giving a value from 0 (11111) to 20 (55555). We’d expect this measure of severity of states to correlate strongly with the mean utility values derived from the composite time trade-off (TTO) exercise.

Taking Singapore as an example, the mean of positive values (states better than dead) decreased from 0.89 to 0.21 with increasing severity, which is reassuring. The mean of negative values, on the other hand, ranged from -0.98 to -0.89. Negative values were clustered between -0.5 and -1.0. Results were similar across the other countries. In all except Thailand, observed negative values were indistinguishable from random noise. There was no decreasing trend in mean utility values as severity increased for states worse than dead. A linear mixed model with participant-specific intercepts and an ANOVA model confirmed the findings.

What this means is that we can’t say much about states worse than dead except that they are worse than dead. How much worse doesn’t relate to severity, which is worrying if we’re using these values in trade-offs against states better than dead. Mostly, the authors frame this lack of discriminative ability as a practical problem, rather than anything more fundamental. The discussion section provides some interesting speculation, but my favourite part of the paper is an analogy, which I’ll be quoting in future: “it might be worse to be lost at sea in deep waters than in a pond, but not in any way that truly matters”. Dead is dead is dead.

Determining value in health technology assessment: stay the course or tack away? PharmacoEconomics [PubMed] Published 9th November 2018

The cost-per-QALY approach to value in health care is no stranger to assault. The majority of criticisms are ill-founded special pleading, but, sometimes, reasonable tweaks and alternatives have been proposed. The aim of this paper was to bring together a supergroup of health economists to review and discuss these reasonable alternatives. Specifically, the questions they sought to address were: i) what should health technology assessment achieve, and ii) what should be the approach to value-based pricing?

The paper provides an unstructured overview of a selection of possible adjustments or alternatives to the cost-per-QALY method. We’re very briefly introduced to QALY weighting, efficiency frontiers, and multi-criteria decision analysis. The authors don’t tell us why we ought (or ought not) to adopt these alternatives. I was hoping that the paper would provide tentative answers to the normative questions posed, but it doesn’t do that. It doesn’t even outline the thought processes required to answer them.

The purpose of this paper seems to be to argue that alternative approaches aren’t sufficiently developed to replace the cost-per-QALY approach. But it’s hardly a strong defence. I’m a big fan of the cost-per-QALY as a necessary (if not sufficient) part of decision making in health care, and I agree with the authors that the alternatives are lacking in support. But the lack of conviction in this paper scares me. It’s tempting to make a comparison between the EU and the QALY.

How can we evaluate the cost-effectiveness of health system strengthening? A typology and illustrations. Social Science & Medicine [PubMed] Published 3rd November 2018

Health care is more than the sum of its parts. This is particularly evident in low- and middle-income countries that might lack strong health systems and which therefore can’t benefit from a new intervention in the way a strong system could. Thus, there is value in health system strengthening. But, as the authors of this paper point out, this value can be difficult to identify. The purpose of this study is to provide new methods to model the impact of health system strengthening in order to support investment decisions in this context.

The authors introduce standard cost-effectiveness analysis and economies of scope as relevant pieces of the puzzle. In essence, this paper is trying to marry the two. An intervention is more likely to be cost-effective if it helps to provide economies of scope, either by making use of an underused platform or providing a new platform that would improve the cost-effectiveness of other interventions. The authors provide a typology with three types of health system strengthening: i) investing in platform efficiency, ii) investing in platform capacity, and iii) investing in new platforms. Examples are provided for each. Simple mathematical approaches to evaluating these are described, using scaling factors and disaggregated cost and outcome constraints. Numerical demonstrations show how these approaches can reveal differences in cost-effectiveness that arise through changes in technical efficiency or the opportunity cost linked to health system strengthening.

This paper is written with international development investment decisions in mind, and in particular the challenge of investments that can mostly be characterised as health system strengthening. But it’s easy to see how many – perhaps all – health services are interdependent. If anything, the broader impact of new interventions on health systems should be considered as standard. The methods described in this paper provide a useful framework to tackle these issues, with food for thought for anybody engaged in cost-effectiveness analysis.

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Thesis Thursday: Anna Heath

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Anna Heath who has a PhD from the University College London. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Bayesian computations for value of information measures using Gaussian processes, INLA and Moment Matching
Supervisors
Gianluca Baio, Ioanna Manolopoulou
Repository link
http://discovery.ucl.ac.uk/id/eprint/10050229

Why are new methods needed for value of information analysis?

Value of Information (VoI) has been around for a really long time – it was first mentioned in a book published in 1959! More recently, it has been suggested that VoI methods can be used in health economics to direct and design future research strategies. There are several different concepts in VoI analysis and each of these can be used to answer different questions. The VoI measure with the most potential calculates the economic benefit of collecting additional data to inform a health economic model (known as the EVSI). The EVSI can be compared with the cost of collecting data and allow us to make sure that our clinical research is “cost-effective”.

The problem is that, mathematically, VoI measures are almost impossible to calculate, so we have to use simulation. Traditionally, these simulation methods have been very slow (in my PhD, one example took over 300 days to compute 10 VoI measures) so we need simulation methods that speed up the computation significantly before VoI can be used for decisions about research design and funding.

Do current EVPPI and EVSI estimation methods give different results?

For most examples, the current estimation methods give similar results but the computational time to obtain these results differs significantly. Since starting my PhD, different estimation methods for the EVPPI and the EVSI have been published. The difference between these methods are the assumptions and the ease of use. The results seem to be pretty stable for all the different methods, which is good!

The EVPPI determines which model parameters have the biggest impact on the cost-effectiveness of the different treatments. This is used to direct possible avenues of future research, i.e. we should focus on gaining more information about parameters with a large impact on cost-effectiveness. The EVPPI is calculated based only on simulations of the model parameters so the number of methods for EVPPI calculation is quite small. To calculate the EVSI, you need to consider how to collect additional data, through a clinical trial, observational study etc, so there is a wider range of available methods.

How does the Gaussian process you develop improve EVPPI estimation?

Before my PhD started, Mark Strong and colleagues at the University of Sheffield developed a method to calculate the EVPPI based on flexible regression. This method is accurate but when you want to calculate the value of a group of model parameters, the computational time increases significantly. A Gaussian process is a method for very flexible regression but could be slow when trying to calculate the EVPPI for a group of parameters. The method we developed adapted the Gaussian process to speed up computation when calculating the EVPPI for a group of parameters. The size of the group of parameters does not really make a difference to the computation for this method, so we allowed for fast EVPPI computation in nearly all practical examples!

What is moment matching, and how can it be used to estimate EVSI?

Moments define the shape of a distribution – the first moment is the mean, the second the variance, the third is the skewness and so on. To estimate the EVSI, we need to estimate a distribution with some specific properties. We can show that this distribution is similar to the distribution of the net benefit from a probabilistic sensitivity analysis. Moment matching is a fancy way of saying that we estimate the EVSI by changing the distribution of the net benefit so it has the same variance as the distribution needed to estimate the EVSI. This significantly decreases the computation time for the EVSI because traditionally we would estimate the distribution for the EVSI using a large number of simulations (I’ve used 10 billion simulations for one estimate).

The really cool thing about this method is that we extended it to use the EVSI to find the trial design and sample size that gives the maximum value for money from research investment resources. The computation time for this analysis was around 5 minutes whereas the traditional method took over 300 days!

Do jobbing health economists need to be experts in value of information analysis to use your BCEA and EVSI software?

The BCEA software uses the costs and effects calculated from a probabilistic health economic model alongside the probabilistic analysis for the model parameters to give standard graphics and summaries. It is based in R and can be used to calculate the EVPPI without being an expert in VoI methods and analysis. All you need is to decide which model parameters you are interested in valuing. We’ve put together a Web interface, BCEAweb, which allows you to use BCEA without using R.

The EVSI software requires a model that incorporates how the data from the future study will be analysed. This can be complicated to design although I’m currently putting together a library of standard examples. Once you’ve designed the study, the software calculates the EVSI without any input from the user, so you don’t need to be an expert in the calculation methods. The software also provides graphics to display the EVSI results and includes text to help interpret the graphical results. An example of the graphical output can be seen here.

Chris Sampson’s journal round-up for 5th November 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.

Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration. The European Journal of Health Economics [PubMed] Published 29th October 2018

Health care is increasingly personalised. This creates the need to evaluate interventions for smaller and smaller subgroups as patient heterogeneity is taken into account. And this usually means we lack the statistical power to have confidence in our findings. The purpose of this paper is to consider the usefulness of a tool that hasn’t previously been employed in economic evaluation – the subpopulation treatment effect pattern plot (STEPP). STEPP works by assessing the interaction between treatments and covariates in different subgroups, which can then be presented graphically. Imagine your X-axis with the values defining the subgroups and your Y-axis showing the treatment outcome. This information can then be used to determine which subgroups exhibit positive outcomes.

This study uses data from a trial-based economic evaluation in heart failure, where patients’ 18-month all-cause mortality risk was estimated at baseline before allocation to one of three treatment strategies. For the STEPP procedure, the authors use baseline risk to define subgroups and adopt net monetary benefit at the patient level as the outcome. The study makes two comparisons (between three alternative strategies) and therefore presents two STEPP figures. The STEPP figures are used to identify subgroups, which the authors apply in a stratified cost-effectiveness analysis, estimating net benefit in each defined risk subgroup.

Interpretation of the STEPPs is a bit loose, with no hard decision rules. The authors suggest that one of the STEPPs shows no clear relationship between net benefit and baseline risk in terms of the cost-effectiveness of the intervention (care as usual vs basic support). The other STEPP shows that, on average, people with baseline risk below 0.16 have a positive net benefit from the intervention (intensive support vs basic support), while those with higher risk do not. The authors evaluate this stratification strategy against an alternative stratification strategy (based on the patient’s New York Heart Association class) and find that the STEPP-based approach is expected to be more cost-effective. So the key message seems to be that STEPP can be used as a basis for defining subgroups as cost-effectively as possible.

I’m unsure about the extent to which this is a method that deserves to have its own name, insofar as it is used in this study. I’ve seen plenty of studies present a graph with net benefit on the Y-axis and some patient characteristic on the X-axis. But my main concern is about defining subgroups on the basis of net monetary benefit rather than some patient characteristic. Is it OK to deny treatment to subgroup A because treatment costs are higher than in subgroup B, even if treatment is cost-effective for the entire population of A+B? Maybe, but I think that creates more challenges than stratification on the basis of treatment outcome.

Using post-market utilisation analysis to support medicines pricing policy: an Australian case study of aflibercept and ranibizumab use. Applied Health Economics and Health Policy [PubMed] Published 25th October 2018

The use of ranibizumab and aflibercept has been a hot topic in the UK, where NHS providers feel that they’ve been bureaucratically strong-armed into using an incredibly expensive drug to treat certain eye conditions when a cheaper and just-as-effective alternative is available. Seeing how other countries have managed prices in this context could, therefore, be valuable to the NHS and other health services internationally. This study uses data from Australia, where decisions about subsidising medicines are informed by research into how drugs are used after they come to market. Both ranibizumab (in 2007) and aflibercept (in 2012) were supported for the treatment of age-related macular degeneration. These decisions were based on clinical trials and modelling studies, which also showed that the benefit of ~6 aflibercept prescriptions equated to the benefit of ~12 ranibizumab prescriptions, justifying a higher price-per-injection for aflibercept.

In the UK and US, aflibercept attracts a higher price. The authors assume that this is because of the aforementioned trial data relating to the number of doses. However, in Australia, the same price is paid for aflibercept and ranibizumab. This is because a post-market analysis showed that, in practice, ranibizumab and aflibercept had a similar dose frequency. The purpose of this study is to see whether this is because different groups of patients are being prescribed the two drugs. If they are, then we might anticipate heterogenous treatment outcomes and thus a justification for differential pricing. Data were drawn from an administrative claims database for 208,000 Australian veterans for 2007-2017. The monthly number of aflibercept and ranibizumab prescriptions was estimated for each person, showing that total prescriptions increased steadily over the period, with aflibercept taking around half the market within a year of its approval. Ranibizumab initiators were slightly older in the post-aflibercept era but, aside from that, there were no real differences identified. When it comes to the prescription of ranibizumab or aflibercept, gender, being in residential care, remoteness of location, and co-morbidities don’t seem to be important. Dispensing rates were similar, at around 3 prescriptions during the first 90 days and around 9 prescriptions during the following 12 months.

The findings seem to support Australia’s decision to treat ranibizumab and aflibercept as substitutes at the same price. More generally, they support the idea that post-market utilisation assessments can (and perhaps should) be used as part of the health technology assessment and reimbursement process.

Do political factors influence public health expenditures? Evidence pre- and post-great recession. The European Journal of Health Economics [PubMed] Published 24th October 2018

There is mixed evidence about the importance of partisanship in public spending, and very little relating specifically to health care. I’d be worried if political factors didn’t influence public spending on health, given that that’s a definitively political issue. How the situation might be different before and after a recession is an interesting question.

The authors combined OECD data for 34 countries from 1970-2016 with the Database of Political Institutions. This allowed for the creation of variables relating to the ideology of the government and the proximity of elections. Stationary panel data models were identified as the most appropriate method for analysis of these data. A variety of political factors were included in the models, for which the authors present marginal effects. The more left-wing a government, the higher is public spending on health care, but this is only statistically significant in the period before the crisis of 2007. Before the crisis, coalition governments tended to spend more, while governments with more years in office tended to spend less. These effects also seem to disappear after 2007. Throughout the whole period, governing parties with a stronger majority tended to spend less on health care. Several of the non-political factors included in the models show the results that we would expect. GDP per capita is positively associated with health care expenditures, for example. The findings relating to the importance of political factors appear to be robust to the inclusion of other (non-political) variables and there are similar findings when the authors look at public health expenditure as a percentage of total health expenditure. In contradiction with some previous studies, proximity to elections does not appear to be important.

The most interesting finding here is that the effect of partisanship seems to have mostly disappeared – or, at least, reduced – since the crisis of 2007. Why did left-wing parties and right-wing parties converge? The authors suggest that it’s because adverse economic circumstances restrict the extent to which governments can make decisions on the basis of ideology. Though I dare say readers of this blog could come up with plenty of other (perhaps non-economic) explanations.

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