Chris Sampson’s journal round-up for 11th March 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.

Identification, review, and use of health state utilities in cost-effectiveness models: an ISPOR Good Practices for Outcomes Research Task Force report. Value in Health [PubMed] Published 1st March 2019

When modellers select health state utility values to plug into their models, they often do it in an ad hoc and unsystematic way. This ISPOR Task Force report seeks to address that.

The authors discuss the process of searching, reviewing, and synthesising utility values. Searches need to use iterative techniques because evidence requirements develop as a model develops. Due to the scope of models, it may be necessary to develop multiple search strategies (for example, for different aspects of disease pathways). Searches needn’t be exhaustive, but they should be systematic and transparent. The authors provide a list of factors that should be considered in defining search criteria. In reviewing utility values, both quality and appropriateness should be considered. Quality is indicated by the precision of the evidence, the response rate, and missing data. Appropriateness relates to the extent to which the evidence being reviewed conforms to the context of the model in which it is to be used. This includes factors such as the characteristics of the study population, the measure used, value sets used, and the timing of data collection. When it comes to synthesis, the authors suggest it might not be meaningful in most cases, because of variation in methods. We can’t pool values if they aren’t (at least roughly) equivalent. Therefore, one approach is to employ strict inclusion criteria (e.g only EQ-5D, only a particular value set), but this isn’t likely to leave you with much. Meta-regression can be used to analyse more dissimilar utility values and provide insight into the impact of methodological differences. But the extent to which this can provide pooled values for a model is questionable, and the authors concede that more research is needed.

This paper can inform that future research. Not least in its attempt to specify minimum reporting standards. We have another checklist, with another acronym (SpRUCE). The idea isn’t so much that this will guide publications of systematic reviews of utility values, but rather that modellers (and model reviewers) can use it to assess whether the selection of utility values was adequate. The authors then go on to offer methodological recommendations for using utility values in cost-effectiveness models, considering issues such as modelling technique, comorbidities, adverse events, and sensitivity analysis. It’s early days, so the recommendations in this report ought to be changed as methods develop. Still, it’s a first step away from the ad hoc selection of utility values that (no doubt) drives the results of many cost-effectiveness models.

Estimating the marginal cost of a life year in Sweden’s public healthcare sector. The European Journal of Health Economics [PubMed] Published 22nd February 2019

It’s only recently that health economists have gained access to data that enables the estimation of the opportunity cost of health care expenditure on a national level; what is sometimes referred to as a supply-side threshold. We’ve seen studies in the UK, Spain, Australia, and here we have one from Sweden.

The authors use data on health care expenditure at the national (1970-2016) and regional (2003-2016) level, alongside estimates of remaining life expectancy by age and gender (1970-2016). First, they try a time series analysis, testing the nature of causality. Finding an apparently causal relationship between longevity and expenditure, the authors don’t take it any further. Instead, the results are based on a panel data analysis, employing similar methods to estimates generated in other countries. The authors propose a conceptual model to support their analysis, which distinguishes it from other studies. In particular, the authors assert that the majority of the impact of expenditure on mortality operates through morbidity, which changes how the model should be specified. The number of newly graduated nurses is used as an instrument indicative of a supply-shift at the national rather than regional level. The models control for socioeconomic and demographic factors and morbidity not amenable to health care.

The authors estimate the marginal cost of a life year by dividing health care expenditure by the expenditure elasticity of life expectancy, finding an opportunity cost of €38,812 (with a massive 95% confidence interval). Using Swedish population norms for utility values, this would translate into around €45,000/QALY.

The analysis is considered and makes plain the difficulty of estimating the marginal productivity of health care expenditure. It looks like a nail in the coffin for the idea of estimating opportunity costs using time series. For now, at least, estimates of opportunity cost will be based on variation according to geography, rather than time. In their excellent discussion, the authors are candid about the limitations of their model. Their instrument wasn’t perfect and it looks like there may have been important confounding variables that they couldn’t control for.

Frequentist and Bayesian meta‐regression of health state utilities for multiple myeloma incorporating systematic review and analysis of individual patient data. Health Economics [PubMed] Published 20th February 2019

The first paper in this round-up was about improving practice in the systematic review of health state utility values, and it indicated the need for more research on the synthesis of values. Here, we have some. In this study, the authors conduct a meta-analysis of utility values alongside an analysis of registry and clinical study data for multiple myeloma patients.

A literature search identified 13 ‘methodologically appropriate’ papers, providing 27 health state utility values. The EMMOS registry included data for 2,445 patients in 22 counties and the APEX clinical study included 669 patients, all with EQ-5D-3L data. The authors implement both a frequentist meta-regression and a Bayesian model. In both cases, the models were run including all values and then with a limited set of only EQ-5D values. These models predicted utility values based on the number of treatment classes received and the rate of stem cell transplant in the sample. The priors used in the Bayesian model were based on studies that reported general utility values for the presence of disease (rather than according to treatment).

The frequentist models showed that utility was low at diagnosis, higher at first treatment, and lower at each subsequent treatment. Stem cell transplant had a positive impact on utility values independent of the number of previous treatments. The results of the Bayesian analysis were very similar, which the authors suggest is due to weak priors. An additional Bayesian model was run with preferred data but vague priors, to assess the sensitivity of the model to the priors. At later stages of disease (for which data were more sparse), there was greater uncertainty. The authors provide predicted values from each of the five models, according to the number of treatment classes received. The models provide slightly different results, except in the case of newly diagnosed patients (where the difference was 0.001). For example, the ‘EQ-5D only’ frequentist model gave a value of 0.659 for one treatment, while the Bayesian model gave a value of 0.620.

I’m not sure that the study satisfies the recommendations outlined in the ISPOR Task Force report described above (though that would be an unfair challenge, given the timing of publication). We’re told very little about the nature of the studies that are included, so it’s difficult to judge whether they should have been combined in this way. However, the authors state that they have made their data extraction and source code available online, which means I could check that out (though, having had a look, I can’t find the material that the authors refer to, reinforcing my hatred for the shambolic ‘supplementary material’ ecosystem). The main purpose of this paper is to progress the methods used to synthesise health state utility values, and it does that well. Predictably, the future is Bayesian.

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Thesis Thursday: Logan Trenaman

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 Logan Trenaman who has a PhD from the University of British Columbia. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Economic evaluation of interventions to support shared decision-making: an extension of the valuation framework
Supervisors
Nick Bansback, Stirling Bryan
Repository link
http://hdl.handle.net/2429/66769

What is shared decision-making?

Shared decision-making is a process whereby patients and health care providers work together to make decisions. For most health care decisions, where there is no ‘best’ option, the most appropriate course of action depends on the clinical evidence and the patient’s informed preferences. In effect, shared decision-making is about reducing information asymmetry, by allowing providers to inform patients about the potential benefits and harms of alternative tests or treatments, and patients to express their preferences to their provider. The goal is to reach agreement on the most appropriate decision for that patient.

My thesis focused on individuals with advanced osteoarthritis who were considering whether to undergo total hip or knee replacement, or use non-surgical treatments such as pain medication, exercise, or mobility aids. Joint replacement alleviates pain and improves mobility for most patients, however, as many as 20-30% of recipients have reported insignificant improvement in symptoms and/or dissatisfaction with results. Shared decision-making can help ensure that those considering joint replacement are aware of alternative treatments and have realistic expectations about the potential benefits and harms of each option.

There are different types of interventions available to help support shared decision-making, some of which target the patient (e.g. patient decision aids) and some of which target providers (e.g. skills training). My thesis focused on a randomized controlled trial that evaluated a pre-consultation patient decision aid, which generated a summary report for the surgeon that outlined the patient’s knowledge, values, and preferences.

How can the use of decision aids influence health care costs?

The use of patient decision aids can impact health care costs in several ways. Some patient decision aids, such as those evaluated in my thesis, are designed for use by patients in preparation for a consultation where a treatment decision is made. Others are designed to be used during the consultation with the provider. There is some evidence that decision aids may increase up-front costs, by increasing the length of consultations, requiring investments to integrate decision aids into routine care, or train clinicians. These interventions may impact downstream costs by influencing treatment decision-making. For example, the Cochrane review of patient decision aids found that, across 18 studies in major elective surgery, those exposed to decision aids were less likely to choose surgery compared to those in usual care (RR: 0.86, 95% CI: 0.75 to 1.00).

This was observed in the trial-based economic evaluation which constituted the first chapter of my thesis. This analysis found that decision aids were highly cost-effective, largely due to a smaller proportion of patients undergoing joint replacement. Of course, this conclusion could change over time. One of the challenges of previous cost-effectiveness analysis (CEA) of patient decision aids has been a lack of long-term follow-up. Patients who choose not to have surgery over the short-term may go on to have surgery later. To look at the longer-term impact of decision aids, the third chapter of my thesis linked trial participants to administrative data with an average of 7-years follow-up. I found that, from a resource use perspective, the conclusion was the same as observed during the trial: fewer patients exposed to decision aids had undergone surgery, resulting in lower costs.

What is it about shared decision-making that patients value?

On the whole, the evidence suggests that patients value being informed, listened to, and offered the opportunity to participate in decision-making (should they wish!). To better understand how much shared decision-making is valued, I performed a systematic review of discrete choice experiments (DCEs) that had valued elements of shared decision-making. This review found that survey respondents (primarily patients) were willing to wait longer, pay, and in some cases willing to accept poorer health outcomes for greater shared decision-making.

It is important to consider preference heterogeneity in this context. The last chapter of my PhD performed a DCE to value shared decision-making in the context of advanced knee osteoarthritis. The DCE included three attributes: waiting time, health outcomes, and shared decision-making. The latent class analysis found four distinct subgroups of patients. Two groups were balanced, and traded between all attributes, while one group had a strong preference for shared decision-making, and another had a strong preference for better health outcomes. One important finding from this analysis was that having a strong preference for shared decision-making was not associated with demographic or clinical characteristics. This highlights the importance of each clinical encounter in determining the appropriate level of shared decision-making for each patient.

Is it meaningful to estimate the cost-per-QALY of shared decision-making interventions?

One of the challenges of my thesis was grappling with the potential conflict between the objectives of CEA using QALYs (maximizing health) and shared decision-making interventions (improved decision-making). Importantly, encouraging shared decision-making may result in patients choosing alternatives that do not maximize QALYs. For example, informed patients may choose to delay or forego elective surgery due to potential risks, despite it providing more QALYs (on average).

In cases where a CEA finds that shared decision-making interventions result in poorer health outcomes at lower cost, I think this is perfectly acceptable (provided patients are making informed choices). However, it becomes more complicated when shared decision-making interventions increase costs, result in poorer health outcomes, but provide other, non-health benefits such as informing patients or involving them in treatment decisions. In such cases, decision-makers need to consider whether it is justified to allocate scarce health care resources to encourage shared decision-making when it requires sacrificing health outcomes elsewhere. The latter part of my thesis tried to inform this trade-off, by valuing the non-health benefits of shared decision-making which would not otherwise be captured in a CEA that uses QALYs.

How should the valuation framework be extended, and is this likely to indicate different decisions?

I extended the valuation framework by attempting to value non-health benefits of shared decision-making. I followed guidelines from the Canadian Agency for Drugs and Technologies in Health, which state that “the value of non-health effects should be based on being traded off against health” and that societal preferences be used for this valuation. Requiring non-health benefits to be valued relative to health reflects the opportunity cost of allocating resources toward these outcomes. While these guidelines do not specifically state how to do so, I chose to value shared decision-making relative to life-years using a chained (or two-stage) valuation approach so that they could be incorporated within the QALY.

Ultimately, I found that the value of the process of shared decision-making was small, however, this may have an impact on cost-effectiveness. The reasons for this are twofold. First, there are few cases where shared decision-making interventions improve health outcomes. A 2018 sub-analysis of the Cochrane review of patient decision aids found little evidence that they impact health-related quality of life. Secondly, the up-front cost of implementing shared decision-making interventions may be small. Thus, in cases where shared decision-making interventions require a small investment but provide no health benefit, the non-health value of shared decision-making may impact cost-effectiveness. One recent example from Dr Victoria Brennan found that incorporating process utility associated with improved consultation quality, resulting from a new online assessment tool, increased the probability that the intervention was cost-effective from 35% to 60%.

Chris Sampson’s journal round-up for 18th February 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.

An educational review about using cost data for the purpose of cost-effectiveness analysis. PharmacoEconomics [PubMed] Published 12th February 2019

Costing can seem like a cinderella method in the health economist’s toolkit. If you’re working on an economic evaluation, estimating resource use and costs can be tedious. That is perhaps why costing methodology has been relatively neglected in the literature compared to health state valuation (for example). This paper tries to redress the balance slightly by providing an overview of the main issues in costing, explaining why they’re important, so that we can do a better job. The issues are more complex than many assume.

Supported by a formidable reference list (n=120), the authors tackle 9 issues relating to costing: i) costs vs resource use; ii) trial-based vs model-based evaluations; iii) costing perspectives; iv) data sources; v) statistical methods; vi) baseline adjustments; vii) missing data; viii) uncertainty; and ix) discounting, inflation, and currency. It’s a big paper with a lot to say, so it isn’t easily summarised. Its role is as a reference point for us to turn to when we need it. There’s a stack of papers and other resources cited in here that I wasn’t aware of. The paper itself doesn’t get technical, leaving that to the papers cited therein. But the authors provide a good discussion of the questions that ought to be addressed by somebody designing a study, relating to data collection and analysis.

The paper closes with some recommendations. The main one is that people conducting cost-effectiveness analysis should think harder about why they’re making particular methodological choices. The point is also made that new developments could change the way we collect and analyse cost data. For example, the growing use of observational data demands that greater consideration be given to unobserved confounding. Costing methods are important and interesting!

A flexible open-source decision model for value assessment of biologic treatment for rheumatoid arthritis. PharmacoEconomics [PubMed] Published 9th February 2019

Wherever feasible, decision models should be published open-source, so that they can be reviewed, reused, recycled, or, perhaps, rejected. But open-source models are still a rare sight. Here, we have one for rheumatoid arthritis. But the paper isn’t really about the model. After all, the model and supporting documentation are already available online. Rather, the paper describes the reasoning behind publishing a model open-source, and the process for doing so in this case.

This is the first model released as part of the Open Source Value Project, which tries to convince decision-makers that cost-effectiveness models are worth paying attention to. That is, it’s aimed at the US market, where models are largely ignored. The authors argue that models need to be flexible to be valuable into the future and that, to achieve this, four steps should be followed in the development: 1) release the initial model, 2) invite feedback, 3) convene an expert panel to determine actions in light of the feedback, and 4) revise the model. Then, repeat as necessary. Alongside this, people with the requisite technical skills (i.e. knowing how to use R, C++, and GitHub) can proffer changes to the model whenever they like. This paper was written after step 3 had been completed, and the authors report receiving 159 comments on their model.

The model itself (which you can have a play with here) is an individual patient simulation, which is set-up to evaluate a variety of treatment scenarios. It estimates costs and (mapped) QALYs and can be used to conduct cost-effectiveness analysis or multi-criteria decision analysis. The model was designed to be able to run 32 different model structures based on different assumptions about treatment pathways and outcomes, meaning that the authors could evaluate structural uncertainties (which is a rare feat). A variety of approaches were used to validate the model.

The authors identify several challenges that they experienced in the process, including difficulties in communication between stakeholders and the large amount of time needed to develop, test, and describe a model of this sophistication. I would imagine that, compared with most decision models, the amount of work underlying this paper is staggering. Whether or not that work is worthwhile depends on whether researchers and policymakers make us of the model. The authors have made it as easy as possible for stakeholders to engage with and build on their work, so they should be hopeful that it will bear fruit.

EQ-5D-Y-5L: developing a revised EQ-5D-Y with increased response categories. Quality of Life Research [PubMed] Published 9th February 2019

The EQ-5D-Y has been a slow burner. It’s been around 10 years since it first came on the scene, but we’ve been without a value set and – with the introduction of the EQ-5D-5L – the questionnaire has lost some comparability with its adult equivalent. But the EQ-5D-Y has almost caught-up, and this study describes part of how that’s been achieved.

The reason to develop a 5L version for the EQ-5D-Y is the same as for the adult version – to reduce ceiling effects and improve sensitivity. A selection of possible descriptors was identified through a review of the literature. Focus groups were conducted with children between 8 and 15 years of age in Germany, Spain, Sweden, and the UK in order to identify labels that can be understood by young people. Specifically, the researchers wanted to know the words used by children and adolescents to describe the quantity or intensity of health problems. Participants ranked the labels according to severity and specified which labels they didn’t like. Transcripts were analysed using thematic content analysis. Next, individual interviews were conducted with 255 participants across the four countries, which involved sorting and response scaling tasks. Younger children used a smiley scale. At this stage, both 4L and 5L versions were being considered. In a second phase of the research, cognitive interviews were used to test for comprehensibility and feasibility.

A 5-level version was preferred by most, and 5L labels were identified in each language. The English version used terms like ‘a little bit’, ‘a lot’, and ‘really’. There’s plenty more research to be done on the EQ-5D-Y-5L, including psychometric testing, but I’d expect it to be coming to studies near you very soon. One of the key takeaways from this study, and something that I’ve been seeing more in research in recent years, is that kids are smart. The authors make this point clear, particulary with respect to the response scaling tasks that were conducted with children as young as 8. Decision-making criteria and frameworks that relate to children should be based on children’s preferences and ideas.

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