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.

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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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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