R for trial and model-based cost-effectiveness analysis: workshop

Background and objectives

It is our pleasure to announce a workshop and training event on the use of R for trial and model-based cost-effectiveness analysis (CEA). This follows our successful workshop on R for CEA in 2018.

Our event will begin with a half-day short course on R for decision trees and Markov models and the use of the BCEA package for graphical and statistical analysis of results; this will be delivered by Gianluca Baio of UCL and Howard Thom of Bristol University.

This will be followed by a one-day workshop in which we will present a wide variety of technical aspects by experts from academia, industry, and government institutions (including NICE). Topics will include decision trees, Markov models, discrete event simulation, integration of network meta-analysis, extrapolation of survival curves, and development of R packages.

We will include a pre-workshop virtual code challenge on a problem set by our scientific committee. This will take place over Github and a Slack channel with participants encouraged to submit final R code solutions for peer review on efficiency, flexibility, elegance and transparency. Prizes will be provided for the best entry.

Participants are also invited to submit abstracts for potential oral presentations. An optional dinner and networking event will be held on the evening of 8th July.

Registration is open until 1 June 2019 at https://onlinestore.ucl.ac.uk/conferences-and-events/faculty-of-mathematical-physical-sciences-c06/department-of-statistical-science-f61/f61-workshop-on-r-for-trial-modelbased-costeffectiveness-analysis

To submit an abstract, please send it to howard.thom@bristol.ac.uk with the subject “R for CEA abstract”. The word limit is 300. Abstract submission deadline is 15 May 2019 and the scientific committee will make decisions on acceptance by 1st June 2018.

Preliminary Programme

Day 2: Workshop. Tuesday 9th July.

  • 9:30-9:45. Howard Thom. Welcome
  • 9:45-10:15. Nathan Green. Imperial College London. _Simple, pain-free decision trees in R for the Excel user
  • 10:15-10:35 Pedro Saramago. Centre for Health Economics, University of York. Using R for Markov modelling: an introduction
  • 10:35-10:55. Alison Smith. University of Leeds. Discrete event simulation models in R
  • 10:55-11:10. Coffee
  • 11:10-12:20. Participants oral presentation session (4 speakers, 15 minutes each)
  • 12:20-13:45. Lunch
  • 13:45-14:00. Gianluca Baio. University College London. Packing up, shacking up’s (going to be) all you wanna do!. Building packages in R and Github
  • 14:00-14:15. Jeroen Jansen. Innovation and Value Initiative. State transition models and integration with network meta-analysis
  • 14:15-14:25. Ash Bullement. Delta Hat Analytics, UK. Fitting and extrapolating survival curves for CEA models
  • 14:25-14:45. Iryna Schlackow. Nuffield Department of Public Health, University of Oxford. Generic R methods to prepare routine healthcare data for disease modelling
  • 14:45-15:00. Coffee
  • 15:00-15:15. Initiatives for the future and challenges in gaining R acceptance (ISPOR Taskforce, ISPOR Special Interest Group, future of the R for CEA workshop)
  • 15:15-16:30. Participant discussion.
  • 16:30-16:45. Anthony Hatswell. Close and conclusions

 

R for trial and model-based cost-effectiveness analysis: short course

Background and objectives

It is our pleasure to announce a workshop and training event on the use of R for trial and model-based cost-effectiveness analysis (CEA). This follows our successful workshop on R for CEA in 2018.

Our event will begin with a half-day short course on R for decision trees and Markov models and the use of the BCEA package for graphical and statistical analysis of results; this will be delivered by Gianluca Baio of UCL and Howard Thom of Bristol University.

This will be followed by a one-day workshop in which we will present a wide variety of technical aspects by experts from academia, industry, and government institutions (including NICE). Topics will include decision trees, Markov models, discrete event simulation, integration of network meta-analysis, extrapolation of survival curves, and development of R packages.

We will include a pre-workshop virtual code challenge on a problem set by our scientific committee. This will take place over Github and a Slack channel with participants encouraged to submit final R code solutions for peer review on efficiency, flexibility, elegance and transparency. Prizes will be provided for the best entry.

Participants are also invited to submit abstracts for potential oral presentations. An optional dinner and networking event will be held on the evening of 8th July.

Registration is open until 1 June 2019 at https://onlinestore.ucl.ac.uk/conferences-and-events/faculty-of-mathematical-physical-sciences-c06/department-of-statistical-science-f61/f61-short-course-on-r-for-decision-trees-markov-models-the-use-of-bcea

 

Preliminary Programme

Day 1: Introduction to R for Cost-Effectiveness Modelling. Monday 8th July.

  • 13:00-13:15. Howard Thom. Welcome and introductions
  • 13:15-13:45. Howard Thom. Building a decision tree in R
  • 13:45-14:15. Gianluca Baio. Using BCEA to summarise outputs of an economic model
  • 14:15-14:45. Practical 1 (Decision trees)
  • 14:45-15:00. Coffee break
  • 15:00-15:45. Howard Thom. R for building Markov models
  • 15:45-16:15. Gianluca Baio. Further use of BCEA
  • 16:15-17:00. Practical 2 (Markov models)

Chris Sampson’s journal round-up for 1st April 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.

Toward a centralized, systematic approach to the identification, appraisal, and use of health state utility values for reimbursement decision making: introducing the Health Utility Book (HUB). Medical Decision Making [PubMed] Published 22nd March 2019

Every data point reported in research should be readily available to us all in a structured knowledge base. Most of us waste most of our time retreading old ground, meaning that we don’t have the time to do the best research possible. One instance of this is in the identification of health state utility values to plug into decision models. Everyone who builds a model in a particular context goes searching for utility values – there is no central source. The authors of this paper are hoping to put an end to that.

The paper starts with an introduction to the importance of health state utility values in cost-effectiveness analysis, which most of us don’t need to read. Of course, the choice of utility values in a model is very important and can dramatically alter estimates of cost-effectiveness. The authors also discuss issues around the identification of utility values and the assessment of their quality and applicability. Then we get into the objectives of the ‘Health Utility Book’, which is designed to tackle these issues.

The Health Utility Book will consist of a registry (I like registries), backed by a systematic approach to the identification and inclusion (registration?) of utility values. The authors plan to develop a quality assessment tool for studies that report utility values, using a Delphi panel method to identify appropriate indicators of quality to be included. The quality assessment tool will be complemented by a tool to assess applicability, which will be developed through interviews with stakeholders involved in the reimbursement process.

In the first place, the Health Utility Book will only compile utility values for cancer, and some of the funding for the project is cancer specific. To survive, the project will need more money from more sources. To be sustainable, the project will need to attract funding indefinitely. Or perhaps it could morph into a crowd-sourced platform. Either way, the Health Utility Book has my support.

A review of attitudes towards the reuse of health data among people in the European Union: the primacy of purpose and the common good. Health Policy Published 21st March 2019

We all agree that data protection is important. We all love the GDPR. Organisations such as the European Council and the OECD are committed to facilitating the availability of health data as a means of improving population health. And yet, there often seem to be barriers to accessing health data, and we occasionally hear stories of patients opposing data sharing (e.g. care.data). Maybe people don’t want researchers to be using their data, and we just need to respect that. Or, more likely, we need to figure out what it is that people are opposed to, and design systems that recognise this.

This study reviews research on attitudes towards the sharing of health data for purposes other than treatment, among people living in the EU, employing a ‘configurative literature synthesis’ (a new one for me). From 5,691 abstracts, 29 studies were included. Most related to the use of health data in research in general, while some focused on registries. A few studies looked at other uses, such as for planning and policy purposes. And most were from the UK.

An overarching theme was a low awareness among the population about the reuse of health data. However, in some studies, a desire to be better informed was observed. In general, views towards the use of health data were positive. But this was conditional on the data being used to serve the common good. This includes such purposes as achieving a better understanding of diseases, improving treatments, or achieving more efficient health care. Participants weren’t so happy with health data reuse if it was seen to conflict with the interests of patients providing the data. Commercialisation is a big concern, including the sale of data and private companies profiting from the data. Employers and insurance companies were also considered a threat to patients’ interests. There were conflicting views about whether it is positive for pharmaceutical companies to have access to health data. A minority of people were against sharing data altogether. Certain types of data are seen as being particularly sensitive, including those relating to mental health or sexual health. In general, people expressed concern about data security and the potential for leaks. The studies also looked at the basis for consent that people would prefer. A majority accepted that their data could be used without consent so long as the data were anonymised. But there were no clear tendencies of preference for the various consent models.

It’s important to remember that – on the whole – patients want their data to be used to further the common good. But support can go awry if the data are used to generate profits for private firms or used in a way that might be perceived to negatively affect patients.

Health-related quality of life in injury patients: the added value of extending the EQ-5D-3L with a cognitive dimension. Quality of Life Research [PubMed] Published 18th March 2019

I’m currently working on a project to develop a cognition ‘bolt-on’ for the EQ-5D. Previous research has demonstrated that a cognition bolt-on could provide additional information to distinguish meaningful differences between health states, and that cognition might be a more important candidate than other bolt-ons. Injury – especially traumatic brain injury – can be associated with cognitive impairments. This study explores the value of a cognition bolt-on in this context.

The authors sought to find out whether cognition is sufficiently independent of other dimensions, whether the impact of cognitive problems is reflected in the EuroQol visual analogue scale (EQ VAS), and how a cognition bolt-on affects the overall explanatory power of the EQ-5D-3L. The data used are from the Dutch Injury Surveillance System, which surveys people who have attended an emergency department with an injury, including EQ-5D-3L. The survey adds a cognitive bolt-on relating to memory and concentration.

Data were available for 16,624 people at baseline, with 5,346 complete responses at 2.5-month follow-up. The cognition item was the least affected, with around 20% reporting any problems (though it’s worth noting that the majority of the cohort had injuries to parts of the body other than the head). The frequency of different responses suggests that cognition is dominant over other dimensions in the sense that severe cognitive problems tend to be observed alongside problems in other dimensions, but not vice versa. The mean EQ VAS for people reporting severe cognitive impairment was 41, compared with a mean of 75 for those reporting no problems. Regression analysis showed that moderate and severe cognitive impairment explained 8.7% and 6.2% of the variance of the EQ VAS. Multivariate analysis suggested that the cognitive dimension added roughly the same explanatory power as any other dimension. This was across the whole sample. Interestingly (or, perhaps, worryingly) when the authors looked at the subset of people with traumatic brain injury, the explanatory power of the cognitive dimension was slightly lower than overall.

There’s enough in this paper to justify further research into the advantages and disadvantages of using a cognition bolt-on. But I would say that. Whether or not the bolt-on descriptors used in this study are meaningful to patients remains an open question.

Developing the role of electronic health records in economic evaluation. The European Journal of Health Economics [PubMed] Published 14th March 2019

One way that we can use patients’ routinely collected data is to support the conduct of economic evaluations. In this commentary, the authors set out some of the ways to make the most of these data and discuss some of the methodological challenges. Large datasets have the advantage of being large. When this is combined with the collection of sociodemographic data, estimates for sub-groups can be produced. The data can also facilitate the capture of outcomes not otherwise available. For example, the impact of bariatric surgery on depression outcomes could be identified beyond the timeframe of a trial. The datasets also have the advantage of being representative, where trials are not. This could mean more accurate estimates of costs and outcomes. But there are things to bear in mind when using the data, such as the fact that coding might not always be very accurate, and coding practices could vary between observations. Missing data are likely to be missing for a reason (i.e. not at random), which creates challenges for the analyst. I had hoped that this paper would discuss novel uses of routinely collected data systems, such as the embedding of economic evaluations within them, rather than simply their use to estimate parameters for a model. But if you’re just getting started with using routine data, I suppose you could do worse than start with this paper.

Credits