Tag evidence synthesis

Rita Faria’s journal round-up for 21st October 2019

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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.

Quantifying how diagnostic test accuracy depends on threshold in a meta-analysis. Statistics in Medicine [PubMed] Published 30th September 2019

A diagnostic test is often based on a continuous measure, e.g. cholesterol, which is dichotomised at a certain threshold to classify people as ‘test positive’, who should be treated, or ‘test negative’, who should not. In an economic evaluation, we may wish to compare the costs and benefits of using the test at different thresholds. For example, the cost-effectiveness of offering lipid lowering therapy for people with cholesterol over 7 mmol/L vs over 5 mmol/L. This is straightforward to do if we have access to a large dataset comparing the test to its gold standard to estimate its sensitivity and specificity at various thresholds. It is quite the challenge if we only have aggregate data from multiple publications.

In this brilliant paper, Hayley Jones and colleagues report on a new method to synthesise diagnostic accuracy data from multiple studies. It consists of a multinomial meta-analysis model that can estimate how accuracy depends on the diagnostic threshold. This method produces estimates that can be used to parameterise an economic model.

These new developments in evidence synthesis are very exciting and really important to improve the data going into economic models. My only concern is that the model is implemented in WinBUGS, which is not a software that many applied analysts use. Would it be possible to have a tutorial, or even better, include this method in the online tools available in the Complex Reviews Support Unit website?

Early economic evaluation of diagnostic technologies: experiences of the NIHR Diagnostic Evidence Co-operatives. Medical Decision Making [PubMed] Published 26th September 2019

Keeping with the diagnostic theme, this paper by Lucy Abel and colleagues reports on the experience of the Diagnostic Evidence Co-operatives in conducting early modelling of diagnostic tests. These were established in 2013 to help developers of diagnostic tests link-up with clinical and academic experts.

The paper discusses eight projects where economic modelling was conducted at an early stage of project development. It was fascinating to read about the collaboration between academics and test developers. One of the positive aspects was the buy-in of the developers, while a less positive one was the pressure to produce evidence quickly and that supported the product.

The paper is excellent in discussing the strengths and challenges of these projects. Of note, there were challenges in mapping out a clinical pathway, selecting the appropriate comparators, and establishing the consequences of testing. Furthermore, they found that the parameters around treatment effectiveness were the key driver of cost-effectiveness in many of the evaluations. This is not surprising given that the benefits of a test are usually in better informing the management decisions, rather than via its direct costs and benefits. It definitely resonates with my own experience in conducting economic evaluations of diagnostic tests (see, for example, here).

Following on from the challenges, the authors suggest areas for methodological research: mapping the clinical pathway, ensuring model transparency, and modelling sequential tests. They finish with advice for researchers doing early modelling of tests, although I’d say that it would be applicable to any economic evaluation. I completely agree that we need better methods for economic evaluation of diagnostic tests. This paper is a useful first step in setting up a research agenda.

A second chance to get causal inference right: a classification of data science tasks. Chance [arXiv] Published 14th March 2019

This impressive paper by Miguel Hernan, John Hsu and Brian Healy is an essential read for all researchers, analysts and scientists. Miguel and colleagues classify data science tasks into description, prediction and counterfactual prediction. Description is using data to quantitatively summarise some features of the world. Prediction is using the data to know some features of the world given our knowledge about other features. Counterfactual prediction is using the data to know what some features of the world would have been if something hadn’t happened; that is, causal inference.

I found the explanation of the difference between prediction and causal inference quite enlightening. It is not about the amount of data or the statistical/econometric techniques. The key difference is in the role of expert knowledge. Predicting requires expert knowledge to specify the research question, the inputs, the outputs and the data sources. Additionally, causal inference requires expert knowledge “also to describe the causal structure of the system under study”. This causal knowledge is reflected in the assumptions, the ideas for the data analysis, and for the interpretation of the results.

The section on implications for decision-making makes some important points. First, that the goal of data science is to help people make better decisions. Second, that predictive algorithms can tell us that decisions need to be made but not which decision is most beneficial – for that, we need causal inference. Third, many of us work on complex systems for which we don’t know everything (the human body is a great example). Because we don’t know everything, it is impossible to predict with certainty what would be the consequences of an intervention in a specific individual from routine health records. At most, we can estimate the average causal effect, but even for that we need assumptions. The relevance to the latest developments in data science is obvious, given all the hype around real world data, artificial intelligence and machine learning.

I absolutely loved reading this paper and wholeheartedly recommend it for any health economist. It’s a must read!

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Rita Faria’s journal round-up for 2nd September 2019

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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 24th September 2018

Rita Faria Leave a comment

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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