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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Rita Faria’s journal round-up for 29th 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.

All-male panels and gender diversity of issue panels and plenary sessions at ISPOR Europe. PharmacoEconomics – Open [PubMed] Published 22nd July 2019

All male panels and other diversity considerations for ISPOR. PharmacoEconomics – Open [PubMed] Published 22nd July 2019

How is gender balance at ISPOR Europe conferences? This fascinating paper by Jacoline Bouvy and Michelle Mujoomdar kick-started a debate among the #HealthEconomics Twitterati by showing that the gender distribution is far from balanced.

Jacoline and Michelle found that, between 2016-18, 30% of the 346 speakers at issue panels and plenary sessions were women. Of the 85 panels and sessions, 29% were manels and 64% were mainly composed by men, whereas 2% were all-women panels (‘famels’?).

The ISPOR president Nancy Devlin had a positive and constructive response. For example, I was very pleased to know that ISPOR is taking the issue seriously and no longer has all-male plenary sessions. Issue panels, however, are proposed by members. The numbers show that the gender imbalance in the panels that do get accepted reflects the imbalance of the panels that are proposed.

These two papers raise quite a lot of questions. Why are fewer women participating in abstracts for issue panels? Does the gender distribution in abstracts reflect the distribution in membership, conference attendance, and submission of other types of abstracts? And how does it compare with other conferences in health economics and in other disciplines? Could we learn from other disciplines for effective action? If there is a gender imbalance in conference attendance, providing childcare may help (see here for a discussion). If women tend to submit more abstracts for posters rather than for organised sessions, more networking opportunities both online and at conferences could be an effective action.

I haven’t studied this phenomenon, so I really don’t know. I’d like to suggest that ISPOR starts collecting data systematically and implements initiatives in a way that is amenable to evaluation. After all, doing an evaluation is the health economist way!

Seamless interactive language interfacing between R and Stata. The Stata Journal [RePEc] Published 14th March 2019

Are you a Stata-user, but every so often you’d like to use a function only available in R? This brilliant package is for you!

E.F. Haghish created the rcall package to use R from Stata. It can be used to call R from Stata, or call R for a specific function. With the console mode, we call R to perform an action. The interactive mode allows us to call R from a Stata do-file. The vanilla mode evokes a new R session. The sync mode automatically synchronises objects between R and Stata. Additionally, rcall can transfer various types of data, such as locals, globals, datasets, etc. between Stata and R. Lastly, you can write ado-commands to embed R functions in Stata programs.

This package opens up loads of possibilities. Obviously, it does require that Stata users also know R. But it does make it easy to use R from the comfort of Stata. Looking forward to trying it out more!

Development of the summary of findings table for network meta-analysis. Journal of Clinical Epidemiology [PubMed] Published 2nd May 2019

Whilst the previous paper expands your analytical toolbox, this paper helps you present the results in the context of network meta-analysis. Juan José Yepes-Nuñez and colleagues propose a new summary of findings table to present the results of network meta-analysis. This new table reports all the relevant findings in a way that works for readers.

This study is remarkable because they actually tested the new table with 32 users in four rounds of test and revision. The limitation is that the users were mostly methodologists, although I imagine that recruitment of other users such as clinicians may have been difficult. The new format comprises three sections. The upper section details the PICO (Population; Intervention; Comparison; Outcome) and shows the diagram of the evidence network. The middle section summarises the results in terms of the comparisons, number of studies, participants, relative effect, absolute outcomes and absolute difference, certainty of evidence, rankings, and interpretation of the findings. The lower section defines the terminology and provides some details on the calculations.

It was interesting to read that users felt confused and overwhelmed if the results for all comparisons were shown. Therefore, the table shows the results for one main comparator vs other interventions. The issue is that, as the authors discuss, one comparator needs to be chosen as the main comparator, which is not ideal. Nonetheless, I agree that this is a compromise worth making to achieve a table that works!

I really enjoyed reading about the process to get to this table. I’m wondering if it would be useful to conduct a similar exercise to standardise the presentation of cost-effectiveness results. It would be great to know your thoughts!

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Rita Faria’s journal round-up for 24th 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.

Methodological issues in assessing the economic value of next-generation sequencing tests: many challenges and not enough solutions. Value in Health [PubMed] Published 8th August 2018

This month’s issue of Value in Health includes a themed section on assessing the value of next-generation sequencing. Next-generation sequencing is sometimes hailed as the holy grail in medicine. The promise is that our individual genome can indicate how at-risk we are for many diseases. The question is whether the information obtained by these tests is worth their costs and potentially harmful consequences on well-being and health-related quality of life. This largely remains unexplored, so I expect seeing more economic evaluations of next-generation sequencing in the future.

This paper has caught my eye given an ongoing project on cascade testing protocols for familial hypercholesterolaemia. Next-generation sequencing can be used to identify the genetic cause of familial hypercholesterolaemia, thereby identifying patients suitable to have their relatives tested for the disease. I read this paper with the hope of finding inspiration for our economic evaluation.

This thought-provoking paper discusses the challenges in conducting economic evaluations of next-generation sequencing, such as complex model structure, inclusion of upstream and downstream costs, identifying comparators, identifying costs and outcomes that are related to the test, measuring costs and outcomes, evidence synthesis, data availability and quality.

I agree with the authors that these are important challenges, and it was useful to see them explained in a systematic way. Another valuable feature of this paper is the summary of applied studies which have encountered these challenges and their approaches to overcome them. It’s encouraging to read about how other studies have dealt with complex decision problems!

I’d argue that the challenges are applicable to economic evaluations of many other interventions. For example, identifying the relevant comparators can be a challenge in the evaluations of treatments: in an evaluation of hepatitis C drugs, we compared 633 treatment sequences in 14 subgroups. I view the challenges as the issues to think about when planning an economic evaluation of any intervention: what the comparators are, the scope of the evaluation, the model conceptualisation, data sources and their statistical analysis. Therefore, I’d recommend this paper as an addition to your library about the conceptualisation of economic evaluations.

Compliance with requirement to report results on the EU Clinical Trials Register: cohort study and web resource. BMJ [PubMed] Published 12th September 2018

You may be puzzled at the choice of the latest Ben Goldacre and colleagues’ paper, as it does not include an economic component. This study investigates compliance with the European Commission’s requirements that all trials on the EU Clinical Trials Register post results to the registry within 12 months of completion. At first sight, the economic implications may not be obvious, but they do exist and are quite important.

Clinical trials are a large investment of resources, not only financial but also in the health of patients who accept to take part in an experiment that may impact their health adversely. Therefore, clinical trials can have a huge sunk cost in both money and health. The payoff only realises if the trial is reported. If the trial is not reported, the benefits from the investment cannot be realised. In sum, an unreported trial is clearly a cost-ineffective use of resources.

The solution is simple: ensure that trial results are reported. This way we can all benefit from the information collected by the trial. The issue is, as Goldacre and colleagues have revealed, compliance is far from perfect.

Remarkably, around half of the 7,274 studies are due to publish results. The worst offenders are non-commercial sponsors, where only 11% of trials had their results reported (compared with 68% of trials by a commercial sponsor).

The authors provide a web tool to look up unreported trials by institution. I looked up my very own University of York. It was reassuring to know that my institution has no trials due to report results. Nonetheless, many others are less compliant.

This is an exciting study on the world of clinical trials. I’d suggest that a possible next step would be to estimate the health lost and costs from failing to report trial results.

Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making. Journal of Clinical Epidemiology [PubMed] Published 13th March 2018

Diagnostic tests are an emerging area of methodological development. This timely paper by Rhiannon Owen and colleagues addresses the important topic of evidence synthesis of diagnostic test accuracy studies.

Diagnostic test studies cannot be meta-analysed with the standard techniques used for treatment effectiveness. This is because there are two quantities of interest (sensitivity and specificity), which are correlated, and vary depending on the test threshold (that is, the value at which we say the test result is positive or negative).

Owen and colleagues propose a new approach to synthesising diagnostic test accuracy studies using network meta-analysis methodology. This innovative method allows for comparing multiple tests, evaluated at various test threshold values.

I cannot comment on the method itself as evidence synthesis is not my area of expertise. My interest comes from my experience in the economic evaluation of diagnostic tests, where we often wish to combine evidence from various studies.

With this in mind, I recommend having a look at the NIHR Complex Reviews Support Unit website for more handy tools and the latest research on methods for evidence synthesis. For example, the CRSU has a web tool for meta-analysis of diagnostic tests and a web tool to conduct network meta-analysis for those of us who are not evidence synthesis experts. Providing web tools is a brilliant way of helping analysts using these methods so, hopefully, we’ll see greater use of evidence synthesis in the future.

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