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.
An introduction to metamodeling for reducing computational burden of advanced analyses with health economic models: a structured overview of metamodeling methods in a six-step application process. Medical Decision Making [PubMed] Published 19th May 2020
Metamodels are coming up as a hot topic in cost-effectiveness analysis. A metamodel is a model that approximates the outcome of another model – the simulator model – given the same input. For example, imagine that you have a cost-effectiveness model that takes 1 second to run. Doing PSA over 10,000 simulations will take 10,000 seconds, which is almost 3 hours. Doing EVPPI will take ages! A metamodel could do it much faster. Rather than doing all the calculations that the simulator model does, a metamodel predicts the results.
Koen Degeling and colleagues have given us a much needed guide on metamodeling. They outline a 6-step process to develop a metamodel. First, we need to choose the metamodeling technique. To do that, the authors provide a helpful summary on the various techniques, features, and even R and Python packages.
The second step is to simulate the data set, that is, getting our simulator model to produce outcomes from a set of inputs. We need these data to fit and then to validate our metamodel. Then we can fit our metamodel, which is step 3. Step 4 is to assess the performance of our metamodel and tweaking it to improve its predictions.
Next, we can finally apply our metamodel! We can now conduct analysis that otherwise would have taken years to run. In certain situations, it may be worth doing some additional verifications – this is the sixth step.
This is a really useful paper if you’re foraying into metamodels. It’s a keeper!
The ‘top 10’ challenges for health technology assessment: INAHTA viewpoint. International Journal of Technology Assessment in Health Care [PubMed] Published February 2020
Health technology assessment (HTA) agencies, such as NICE, CADTH, PBAC, and many others, are at the coal face of cost-effectiveness analysis. They have the often difficult task of making decisions about pricing and reimbursement of health technologies, such as new drugs, despite the uncertainties in the evidence and public pressure. Given their incredibly important responsibilities, what are the challenges that HTA agencies face? Brian O’Rourke and colleagues look into this question. They conducted a survey of the HTA agencies that are members of the international network INAHTA. I’ll highlight a couple of findings that stood out for me.
Firstly, the HTA agencies are concerned about the availability, retention, and training of staff. Technical expertise is highly valued, which means that HTA agencies’ salaries may not be able to compete with the private sector. Despite investment in training being recognised as important, “it can be expensive and takes time away from work“.
I completely understand the problem, but I wonder if a possible way forward is to get a better understanding of people’s motivation. For example, Dan Pink argues that people are motivated by autonomy, mastery and purpose. Perhaps HTA agencies struggling to attract and keep skilled staff could ask what else they could offer. Flexible schedules, supporting childcare, time to develop one’s own projects? Rather than seeing training as expensive and time consuming, see training as an essential component of the job. The science is constantly advancing. Skilled staff are essential to appraise the evidence well.
The other challenge that jumped out was how to involve stakeholders in HTA. Short timelines and complex evidence are barriers to engagement of patients, public, and even healthcare professionals and managers. Sitting in the NICE Committee, I sometimes wonder how patient representatives feel when we’re discussing, say, whether a Gompertz or a Weibull is a better fit, when that may well determine whether the drug is recommended. This doesn’t mean that we shouldn’t use the appropriate methods, even if it’s difficult for non-specialists to understand them – we should. Nor that we shouldn’t discuss the evidence with all stakeholders – we most definitely should. For me, it does mean that we should develop effective ways to communicate technical concepts.
Thanks to this paper, we understand the challenges faced by HTA agencies. The next step is much harder but much more important: how do we address these challenges?
Health economic evaluation of screening and treating children with familial hypercholesterolemia early in life: many happy returns on investment? Atherosclerosis Published 19th May 2020
Familial hypercholesterolaemia (FH) is a genetic disease that affects around 1 in 250 people. Due to a faulty gene, people with FH have very high cholesterol, which leads to high risk of cardiovascular disease and premature death. The upside is that FH can be treated effectively and often at little cost with cholesterol lowering treatment, such as statins. The question is, how cost-effective is early diagnosis and treatment?
Zanfina Ademi and colleagues looked at the cost-effectiveness of FH cascade screening with a genetic test in children in Australia. Cascade screening refers to testing relatives of people who are known to have the disease. This is not the first study on FH cascade screening, but it is the first one that considered the impact of high cholesterol from an early age and treatment from childhood. In the model, the effect of lowering cholesterol from childhood on the risk of coronary heart disease later in life was obtained from the effect of having lower cholesterol due to one’s genetic make-up, from a Mendelian randomisation study.
The advantage of this approach is that it accounts for the reduction in risk from early in life. If the authors had used the relationship between cholesterol reduction and cardiovascular risk from RCTs in adults, they might have underestimated the benefits of early treatment. However, there is uncertainty in assuming that the effect of having lower cholesterol from birth due to one’s genetics generalises to lowering cholesterol with drugs.
Zanfina and colleagues found that cascade screening and treatment of children at age 10 improved health outcomes and reduced costs over the long-term, compared to treatment at 18 or 25 years of age. Previous studies of cascade screening of adults found that cascade screening was cost-effective, although it did have greater costs. For children, this important study suggests that the value proposition is even better.
Clearly, cascade screening for FH is cost-effective. But how best to actually do it? For that, you may need to wait for the study that I’m currently working on with colleagues at York, Nottingham, etc. Watch this space!
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