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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Rita Faria’s journal round-up for 2nd 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.

RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ [PubMed] Published 28th August 2019

RCTs are the gold standard primary study to estimate the effect of treatments but are often far from perfect. The question is the extent to which their flaws make a difference to the results. Well, RoB 2 is your new best friend to help answer this question.

Developed by a star-studded team, the RoB 2 is the update to the original risk of bias tool by the Cochrane Collaboration. Bias is assessed by outcome, rather than for the whole RCT. For me, this makes sense.  For example, the primary outcome may be well reported, yet the secondary outcome, which may be the outcome of interest for a cost-effectiveness model, much less so.

Bias is considered in terms of 5 domains, with the overall risk of bias usually corresponding to the worst risk of bias in any of the domains. This overall risk of bias is then reflected in the evidence synthesis, with, for example, a stratified meta-analysis.

The paper is a great read! Jonathan Sterne and colleagues explain the reasons for the update and the process that was followed. Clearly, there was quite a lot of thought given to the types of bias and to develop questions to help reviewers assess it. The only downside is that it may require more time to apply, given that it needs to be done by outcome. Still, I think that’s a price worth paying for more reliable results. Looking forward to seeing it in use!

Characteristics and methods of incorporating randomised and nonrandomised evidence in network meta-analyses: a scoping review. Journal of Clinical Epidemiology [PubMed] Published 3rd May 2019

In keeping with the evidence synthesis theme, this paper by Kathryn Zhang and colleagues reviews how the applied literature has been combining randomised and non-randomised evidence. The headline findings are that combining these two types of study designs is rare and, when it does happen, naïve pooling is the most common method.

I imagine that the limited use of non-randomised evidence is due to its risk of bias. After all, it is difficult to ensure that the measure of association from a non-randomised study is an estimate of a causal effect. Hence, it is worrying that the majority of network meta-analyses that did combine non-randomised studies did so with naïve pooling.

This scoping review may kick start some discussions in the evidence synthesis world. When should we combine randomised and non-randomised evidence? How best to do so? And how to make sure that the right methods are used in practice? As a cost-effectiveness modeller, with limited knowledge of evidence synthesis, I’ve grappled with these questions myself. Do get in touch if you have any thoughts.

A cost-effectiveness analysis of shortened direct-acting antiviral treatment in genotype 1 noncirrhotic treatment-naive patients with chronic hepatitis C virus. Value in Health [PubMed] Published 17th May 2019

Rarely we see a cost-effectiveness paper where the proposed intervention is less costly and less effective, that is, in the controversial southwest quadrant. This exceptional paper by Christopher Fawsitt and colleagues is a welcome exception!

Christopher and colleagues looked at the cost-effectiveness of shorter treatment durations for chronic hepatitis C. Compared with the standard duration, the shorter treatment is not as effective, hence results in fewer QALYs. But it is much cheaper to treat patients over a shorter duration and re-treat those patients who were not cured, rather than treat everyone with the standard duration. Hence, for the base-case and for most scenarios, the shorter treatment is cost-effective.

I’m sure that labelling a less effective and less costly option as cost-effective may have been controversial in some quarters. Some may argue that it is unethical to offer a worse treatment than the standard even if it saves a lot of money. In my view, it is no different from funding better and more costlier treatments, given that the savings will be borne by other patients who will necessarily have access to fewer resources.

The paper is beautifully written and is another example of an outstanding cost-effectiveness analysis with important implications for policy and practice. The extensive sensitivity analysis should provide reassurance to the sceptics. And the discussion is clever in arguing for the value of a shorter duration in resource-constrained settings and for hard to reach populations. A must read!

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Rachel Houten’s journal round-up for 8th July 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.

Adjusting for inflation and currency changes within health economic studies. Value in Health Published 13th June 2019

The purpose of the paper is to highlight the need for transparency in the reporting of methods of currency conversions and adjustments to costs to take inflation into account, in economic evaluations. It chimes with other recent literature which is less prescriptive in terms of providing methods guidelines and more about advocating the “tell us what you did and why” approach. It reminds me of my very first science lesson in high school where we were eager to get our hands on the experiments yet the teacher (met by much eye-rolling) insisted on the importance of describing the methods of any ‘study’. With space at a premium in academic writing, I know, and I’m likely guilty of, some transparency in assumptions being culled, but papers such as this highlight their necessity.

The authors discuss which inflation measure to base the adjustments on, whether to convert local currencies to US or International dollars, three methods of adjusting for inflation, and what to do when costs from other settings are part of the analysis. With a focus on low- and middle-income countries, and using a hypothetical example, the authors demonstrate that employing three different methods of adjusting for inflation can result in a large range in the final estimates.

The authors acknowledge that it is not a one-size-fits-all approach but favour a ‘mixed approach’ where micro-costing is possible and items can be classified as tradable and non-tradable, as they say this is likely to produce the most accurate estimates. However, a study reliant on previously published costing information would need to follow an alternative approach, of which there are two others detailed in the paper.

In terms of working with data from low- and middle-income countries, I can’t say it is my forté. However, the paper summarises the pros and cons of each of their proposed approaches in a straightforward way. The authors include a table that I think would provide an excellent reference point for anyone considering the best approach for their specific set of circumstances.

An updated systematic review of studies mapping (or cross‑walking) measures of health‑related quality of life to generic preference‑based measures to generate utility value. Applied Health Economics and Health Policy [PubMed] [RePEc] Published 3rd April 2019

This is an update of a review of studies published before 2007, which found 30 studies mapping to generic preference-based measures. This latest paper cites 180 included studies with a total of 233 mapping functions reported. The majority of the mapping functions were to the EQ-5D (147 mapping functions) with the second largest group mapping to the SF-6D (45 mapping functions).

Along with an increase in volume of mapping studies since the last review, there has been a marked increase in the different types of regression methods used, which signals a greater consideration of the distribution of the underlying utility data. Reporting on how well the mapping algorithms predict utility in different sub-groups has also increased.

The authors highlight that although mapping can fill an evidence gap, the uncertainty in the estimates is greater than directly measuring health-related quality of life in prospective studies. The authors signpost to ISPOR guidelines for the reporting of mapping studies and emphasise the need to include measurements of error as well as a plot of predicted versus observed values, to enable the user to understand and incorporate the accuracy of the mapping in their economic evaluations.

As stated by the authors, the results of this review provides a useful resource in terms of a catalogue of mapping studies, however it lacks any quality assessment of the studies (also made clear by the authors), so the choice of which mapping algorithm to use is still ours, and takes some thought.  The supplementary Excel file is a great resource to aid the choice as it includes some information about the populations used in the mapping studies alongside the methods, but more studies comparing mapping functions with the same aim against each other would be welcomed.

Investigating the relationship between formal and informal care: an application using panel data for people living together. Health Economics [PubMed] Published 7th June 2019

This paper adds to the literature on informal care by considering co-resident informal care in a UK setting using data from the British Household Panel Survey (BHPS). There has been an increase in the proportion of people receiving non-state provided care in recent years in the UK, and the BHPS also enables the impact of informal care on the use of each of these types of formal care to be explored.

The authors used an instrument for informal care to try to prevent bias due to correlations with other variables such as health. The instrument used for the availability of informal care was the number of adult daughters as it was found to be the most predictive (oh dear, I’ve two sons!). The authors then estimated the impact of informal care on home help, health visitor use, GP visits, and hospital stays.

In this study, informal care was a substitute for both state and non-state home help (with the impact greater for state home help) and complimentary to health visitor use, GP visits, and hospital stays. The authors suggest this may be due to the tasks completed by these different types of service providers and how household tasks are more likely to be undertaken by informal care givers than those more medical in nature. The fact this study considers co-residential care from any household member may explain the stronger substitution effect in this study compared to previous studies looking at informal caregivers living elsewhere as it could be assumed the caregiver residing with the care recipient is more able to provide care.

I find the make-up of households and how that impacts on the need for healthcare resources really interesting, especially as it is generally considered that informal care and the work of charities bolsters the NHS. The results of this study suggest that increases in informal care could generate savings in terms of the need for home help, but an increase in formal care resource use. The reasons for the complimentary relationship between informal care and health visitor, GP, and hospital visits need further exploration.

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