Chris Sampson’s journal round-up for 13th January 2020

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.

A vision ‘bolt-on’ increases the responsiveness of EQ-5D: preliminary evidence from a study of cataract surgery. The European Journal of Health Economics [PubMed] Published 4th January 2020

The EQ-5D is insensitive to differences in how well people can see, despite this seeming to be an important aspect of health. In contexts where the impact of visual impairment may be important, we could potentially use a ‘bolt-on’ item that asks about a person’s vision. I’m working on the development of a vision bolt-on at the moment. But ours won’t be the first. A previously-developed bolt-on has undergone some testing and has been shown to be sensitive to differences between people with different levels of visual function. However, there is little or no evidence to support its responsiveness to changes in visual function, which might arise from treatment.

For this study, 63 individuals were recruited prior to receiving cataract surgery in Singapore. Participants completed the EQ-5D-3L and EQ-5D-5L, both with and without a vision bolt-on, which matched the wording of other EQ-5D dimensions. Additionally, the SF-6D, HUI3, and VF-12 were completed along with a LogMAR assessment of visual acuity. The authors sought to compare the responsiveness of the EQ-5D with a vision bolt-on compared with the standard EQ-5D and the other measures. Therefore, all measures were completed before and after cataract surgery. Preference weights can be generated for the EQ-5D-3L with a vision bolt-on, but they can’t for the EQ-5D-5L, so the authors looked at rescaled sum scores to compare across all measures. Responsiveness was measured using indicators such as standardised effect size and response mean.

Visual acuity changed dramatically before and after surgery, for almost everybody. The authors found that the vision bolt-on does seem to provide a great deal more in the way of response to this, compared to the EQ-5D without the bolt-on. For instance, the mean change in the EQ-5D-3L index score was 0.018 without the vision bolt-on, and 0.031 with it. The HUI3 came out with a mean change of 0.105 and showed the highest responsiveness across all analyses.

Does this mean that we should all be using a vision bolt-on, or perhaps the HUI3? Not exactly. Something I see a lot in papers of this sort – including in this one – is the framing of a “superior responsiveness” as an indication that the measure is doing a better job. That isn’t true if the measure is responding to things to which we don’t want it to respond. As the authors point out, the HUI3 has quite different foundations to the EQ-5D. We also don’t want a situation where analysts can pick and choose measures according to which ever is most responsive to the thing to which they want it to be most responsive. In EuroQol parlance, what goes into the descriptive system is very important.

The causal effect of social activities on cognition: evidence from 20 European countries. Social Science & Medicine Published 9th January 2020

Plenty of studies have shown that cognitive abilities are correlated with social engagement, but few have attempted to demonstrate causality in a large sample. The challenge, of course, is that people who engage in more social activities are likely to have greater cognitive abilities for other reasons, and people’s decision to engage in social activities might depend on their cognitive abilities. This study tackles the question of causality using a novel (to me, at least) methodology.

The analysis uses data from five waves of SHARE (the Survey of Health, Ageing and Retirement in Europe). Survey respondents are asked about whether they engage in a variety of social activities, such as voluntary work, training, sports, or community-related organisations. From this, the authors generate an indicator for people participating in zero, one, or two or more of these activities. The survey also uses a set of tests to measure people’s cognitive abilities in terms of immediate recall capacity, delayed recall capacity, fluency, and numeracy. The authors look at each of these four outcomes, with 231,407 observations for the first three and 124,381 for numeracy (for which the questions were missing from some waves). Confirming previous findings, a strong positive correlation is found between engagement in social activities and each of the cognition indicators.

The empirical strategy, which I had never heard of, is partial identification. This is a non-parametric method that identifies bounds for the average treatment effect. Thus, it is ‘partial’ because it doesn’t identify a point estimate. Fewer assumptions means wider and less informative bounds. The authors start with a model with no assumptions, for which the lower bound for the treatment effect goes below zero. They then incrementally add assumptions. These include i) a monotone treatment response, assuming that social participation does not reduce cognitive abilities on average; ii) monotone treatment selection, assuming that people who choose to be socially active tend to have higher cognitive capacities; iii) a monotone instrumental variable assumption that body mass index is negatively associated with cognitive abilities. The authors argue that their methodology is not likely to be undermined by unobservables, as previous studies might.

The various models show that engaging in social activities has a positive impact on all four of the cognitive indicators. The assumption of monotone treatment response had the highest identifying power. For all models that included this, the 95% confidence intervals in the estimates showed a statistically significant positive impact of social activities on cognition. What is perhaps most interesting about this approach is the huge amount of uncertainty in the estimates. Social activities might have a huge effect on cognition or they might have a tiny effect. A basic OLS-type model, assuming exogenous selection, provides very narrow confidence intervals, whereas the confidence intervals on the partial identification models are almost as wide as the lower and upper band themselves.

One shortcoming of this study for me is that it doesn’t seek to identify the causal channels that have been proposed in previous literature (e.g. loneliness, physical activity, self-care). So it’s difficult to paint a clear picture of what’s going on. But then, maybe that’s the point.

Do research groups align on an intervention’s value? Concordance of cost-effectiveness findings between the Institute for Clinical and Economic Review and other health system stakeholders. Applied Health Economics and Health Policy [PubMed] Published 10th January 2020

Aside from having the most inconvenient name imaginable, ICER has been a welcome edition to the US health policy scene, appraising health technologies in order to provide guidance on coverage. ICER has become influential, with some pharmacy benefit managers using their assessments as a basis for denying coverage for low value medicines. ICER identify technologies as falling in one of three categories – high, low, or intermediate long-term value – according to whether the ICER (grr) falls below, above, or between the threshold range of $50,000-$175,000 per QALY. ICER conduct their own evaluations, but so do plenty of other people. This study sought to find out whether other analyses in the literature agree with ICER’s categorisations.

The authors consider 18 assessments by ICER, including 76 interventions, between 2015 and 2017. For each of these, the authors searched the literature for other comparative studies. Specifically, they went looking for cost-effectiveness analyses that employed the same perspectives and outcomes. Unfortunately, they were only able to identify studies for six disease areas and 14 interventions (of the 76), across 25 studies. It isn’t clear whether this is because there is a lack of literature out there – which would be an interesting finding in itself – or because their search strategy or selection criteria weren’t up to scratch. Of the 14 interventions compared, 10 get a more favourable assessment in the published studies than in their corresponding ICER evaluations, with most being categorised as intermediate value instead of low value. The authors go on to conduct one case study, comparing an ICER evaluation in the context of migraine with a published study by some of the authors of this paper. There were methodological differences. In some respects, it seems as if ICER did a more thorough job, while in other respects the published study seemed to use more defensible assumptions.

I agree with the authors that these kinds of comparisons are important. Not least, we need to be sure that ICER’s approach to appraisal is valid. The findings of this study suggest that maybe ICER should be looking at multiple studies and combining all available data in a more meaningful way. But the authors excluded too many studies. Some imperfect comparisons would have been more useful than exclusion – 14 of 76 is kind of pitiful and probably not representative. And I’m not sure why the authors set out to identify studies that are ‘more favourable’, rather than just different. That perspective seems to reveal an assumption that ICER are unduly harsh in their assessments.

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Rachel Houten’s journal round-up for 11th November 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.

A comparison of national guidelines for network meta-analysis. Value in Health [PubMed] Published October 2019

The evolving treatment landscape results in a greater dependence on indirect treatment comparisons to generate estimates of clinical effectiveness, where the current practice has not been compared to the proposed new intervention in a head-to-head trial. This paper is a review of the guidelines of reimbursement bodies for conducting network meta-analyses. Reassuringly, the authors find that it is possible to meet the needs of multiple agencies with one analysis.

The authors assign three categories to the criteria; “assessment and analysis to test assumptions required for a network meta-analysis, presentation and reporting of results, and justification of modelling choices”, with heterogeneity of the included studies highlighted as one of the key elements to be sure to include if prioritisation of the criteria is necessary. I think this is a simple way of thinking about what needs to be presented but the ‘justification’ category, in my experience, is often given less weight than the other two.

This paper is a useful resource for companies submitting to multiple HTA agencies with the requirements of each national body displayed in tables that are easy to navigate. It meets a practical need but doesn’t really go far enough for me. They do signpost to the PRISMA criteria, but I think it would have been really good to think about the purpose of the submission guidelines; to encourage a logical and coherent summary of the approaches taken so the evidence can be evaluated by decision-makers.

Variation in responsiveness to warranted behaviour change among NHS clinicians: novel implementation of change detection methods in longitudinal prescribing data. BMJ [PubMed] Published 2nd October 2019

I really like this paper. Such a lot of work, from all sectors, is devoted to the production of relevant and timely evidence to inform practice, but if the guidance does not become embedded into the real world then its usefulness is limited.

The authors have managed to utilize a HUGE amount of data to identify the real reaction to two pieces of guidance recommending a change in practice in England. The authors used “trend indicator saturation”, which I’m not ashamed to admit I knew nothing about beforehand, but it is explained nicely. Their thoughtful use of the information available to them results in three indicators of response (in this case the deprescribing of two drugs) around when the change occurs, how quickly it occurs, and how much change occurs.

The authors discover variation in response to the recommendations but suggest an application of their methods could be used to generate feedback to clinicians and therefore drive further response. As some primary care practices took a while to embed the guidance change into their prescribing, the paper raises interesting questions as to where the barriers to the adoption of guidance have occurred.

What is next for patient preferences in health technology assessment? A systematic review of the challenges. Value in Health Published November 2019

It may be that patient preferences have a role to play in the uptake of guideline recommendations, as proposed by the authors of my final paper this week. This systematic review, of the literature around embedding patient preferences into HTA decision-making, groups the discussion in the academic literature into five broad areas; conceptual, normative, procedural, methodological, and practical. The authors state that their purpose was not to formulate their own views, merely to present the available literature, but they do a good job of indicating where to find more opinionated literature on this topic.

Methodological issues were the biggest group, with aspects such as the sample selection, internal and external validity of the preferences generated, and the generalisability of the preferences collected from a sample to the entire population. However, in general, the number of topics covered in the literature is vast and varied.

It’s a great summary of the challenges that are faced, and a ranking based on frequency of topic being mentioned in the literature drives the authors proposed next steps. They recommend further research into the incorporation of preferences within or beyond the QALY and the use of multiple-criteria decision analysis as a method of integrating patient preferences into decision-making. I support the need for “a scientifically and valid manner” to integrate patient preferences into HTA decision-making but wonder if we can first learn of what works well and hasn’t worked so well from the attempts of HTA agencies thus far.

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Chris Sampson’s journal round-up for 30th September 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.

A need for change! A coding framework for improving transparency in decision modeling. PharmacoEconomics [PubMed] Published 24th September 2019

We’ve featured a few papers in recent round-ups that (I assume) will be included in an upcoming themed issue of PharmacoEconomics on transparency in modelling. It’s shaping up to be a good one. The value of transparency in decision modelling has been recognised, but simply making the stuff visible is not enough – it needs to make sense. The purpose of this paper is to help make that achievable.

The authors highlight that the writing of analyses, including coding, involves personal style and preferences. To aid transparency, we need a systematic framework of conventions that make the inner workings of a model understandable to any (expert) user. The paper describes a framework developed by the Decision Analysis in R for Technologies in Health (DARTH) group. The DARTH framework builds on a set of core model components, generalisable to all cost-effectiveness analyses and model structures. There are five components – i) model inputs, ii) model implementation, iii) model calibration, iv) model validation, and v) analysis – and the paper describes the role of each. Importantly, the analysis component can be divided into several parts relating to, for example, sensitivity analyses and value of information analyses.

Based on this framework, the authors provide recommendations for organising and naming files and on the types of functions and data structures required. The recommendations build on conventions established in other fields and in the use of R generally. The authors recommend the implementation of functions in R, and relate general recommendations to the context of decision modelling. We’re also introduced to unit testing, which will be unfamiliar to most Excel modellers but which can be relatively easily implemented in R. The role of various tools are introduced, including R Studio, R Markdown, Shiny, and GitHub.

The real value of this work lies in the linked R packages and other online material, which you can use to test out the framework and consider its application to whatever modelling problem you might have. The authors provide an example using a basic Sick-Sicker model, which you can have a play with using the DARTH packages. In combination with the online resources, this is a valuable paper that you should have to hand if you’re developing a model in R.

Accounts from developers of generic health state utility instruments explain why they produce different QALYs: a qualitative study. Social Science & Medicine [PubMed] Published 19th September 2019

It’s well known that different preference-based measures of health will generate different health state utility values for the same person. Yet, they continue to be used almost interchangeably. For this study, the authors spoke to people involved in the development of six popular measures: QWB, 15D, HUI, EQ-5D, SF-6D, and AQoL. Their goal was to understand the bases for the development of the measures and to explain why the different measures should give different results.

At least one original developer for each instrument was recruited, along with people involved at later stages of development. Semi-structured interviews were conducted with 15 people, with questions on the background, aims, and criteria for the development of the measure, and on the descriptive system, preference weights, performance, and future development of the instrument.

Five broad topics were identified as being associated with differences in the measures: i) knowledge sources used for conceptualisation, ii) development purposes, iii) interpretations of what makes a ‘good’ instrument, iv) choice of valuation techniques, and v) the context for the development process. The online appendices provide some useful tables that summarise the differences between the measures. The authors distinguish between measures based on ‘objective’ definitions (QWB) and items that people found important (15D). Some prioritised sensitivity (AQoL, 15D), others prioritised validity (HUI, QWB), and several focused on pragmatism (SF-6D, HUI, 15D, EQ-5D). Some instruments had modest goals and opportunistic processes (EQ-5D, SF-6D, HUI), while others had grand goals and purposeful processes (QWB, 15D, AQoL). The use of some measures (EQ-5D, HUI) extended far beyond what the original developers had anticipated. In short, different measures were developed with quite different concepts and purposes in mind, so it’s no surprise that they give different results.

This paper provides some interesting accounts and views on the process of instrument development. It might prove most useful in understanding different measures’ blind spots, which can inform the selection of measures in research, as well as future development priorities.

The emerging social science literature on health technology assessment: a narrative review. Value in Health Published 16th September 2019

Health economics provides a good example of multidisciplinarity, with economists, statisticians, medics, epidemiologists, and plenty of others working together to inform health technology assessment. But I still don’t understand what sociologists are talking about half of the time. Yet, it seems that sociologists and political scientists are busy working on the big questions in HTA, as demonstrated by this paper’s 120 references. So, what are they up to?

This article reports on a narrative review, based on 41 empirical studies. Three broad research themes are identified: i) what drove the establishment and design of HTA bodies? ii) what has been the influence of HTA? and iii) what have been the social and political influences on HTA decisions? Some have argued that HTA is inevitable, while others have argued that there are alternative arrangements. Either way, no two systems are the same and it is not easy to explain differences. It’s important to understand HTA in the context of other social tendencies and trends, and that HTA influences and is influenced by these. The authors provide a substantial discussion on the role of stakeholders in HTA and the potential for some to attempt to game the system. Uncertainty abounds in HTA and this necessarily requires negotiation and acts as a limit on the extent to which HTA can rely on objectivity and rationality.

Something lacking is a critical history of HTA as a discipline and the question of what HTA is actually good for. There’s also not a lot of work out there on culture and values, which contrasts with medical sociology. The authors suggest that sociologists and political scientists could be more closely involved in HTA research projects. I suspect that such a move would be more challenging for the economists than for the sociologists.

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