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.

Credits

Meeting round-up: R for Cost-Effectiveness Analysis Workshop 2019

I have switched to using R for my cost-effectiveness models, but I know that I am not using it to its full potential. As a fledgling R user, I was keen to hear about other people’s experiences. I’m in the process of updating one of my models and know that I could be coding things better. But with so many packages and ways to code, the options seem infinite and I struggle to know where to begin. In an attempt to remedy this, I attended the Workshop on R for trial and model-based cost-effectiveness analysis hosted at UCL. I was not disappointed.

The day showcased speakers with varying levels of coding expertise doing a wide range of cool things in R. We started with examples of implementing decision trees using the CEdecisiontree package and cohort Markov models. We also got to hear about a population model using the HEEMOD package, and the purrr package was suggested for probabilistic sensitivity analyses. These talks highlighted how, compared to Excel, R can be reusable, faster, transparent, iterative, and open source.

The open source nature of R, however, has its drawbacks. One of the more interesting conversations that was woven in throughout the day was around the challenges. Can we can trust open-source software? When will NICE begin accepting models coded in R? How important is it that we have models in something like Excel that people can intuitively understand? I’ve not experienced problems choosing to use R for my work; for me, it’s always been around getting the support and information I need to get things done efficiently. The steep learning curve seems to be a major hurdle for many people. I had hoped to attend the short course introduction that was held the day before the workshop, but I was not fast enough to secure my spot as the course sold out within 36 hours. Never fear, the short course will be held again next year in Bristol.

To get around some of the aforementioned barriers to using R, James O’Mahony presented work on an open-source simplified screening model that his team is developing for teaching. An Excel interface with VBA code writes the parameter values in a file that can be imported into R, which has a single file of short model code. Beautiful graphs show the impact of important parameters on the efficiency frontier. He said that they would love to have people look at the code and give suggestions as they want to keep it simple but there is a nonlinear relationship between additional features and complexity.

And then we moved on to more specific topics, such as setting up a community for R users in the NHS, packages for survival curves, and how to build packages in R. I found Gianluca Baio’s presentation on what is a package and why we should be using them really helpful. I realised that I hadn’t really thought about what a package was before (a bundle of code, data, documentation and tests that is easy to share with others) or that it was something that I could or (as he argued) should be thinking about doing for myself as a time-saving tool even if I’m not sharing with others. It’s no longer difficult to build a package when you use packages like devtools and roxygen2 and tools like rstudio and github. He pointed out that packages can be stored on github if you’re not keen to share with the wider world via CRAN.

Another talk that I found particularly helpful was on R methods to prepare routine healthcare data for disease modelling. Claire Simons from The University of Oxford outlined her experiences of using R and ended her talk with a plethora of useful tips. These included using the data.table package for big data sets as it saves time when merging, use meaningful file names to avoid confusion later, and investing in doing things properly from the start as this will save time later. She also suggested using code profiling to identify which code takes the most time. Finally, she reminded us that we should be constantly learning about R: read books on R and writing algorithms and talk to other people who are using R (programmers and other people, not just health economists).

For those who agree that the future is R, check out resources from the Decision Analysis in R for Technologies in Health, a hackathon at Imperial on 6-7th November, hosted by Nathan Green, or join the ISPOR Open Source Models Special Interest Group.

Overall, the workshop structure allowed for a lot of great discussions in a relaxed atmosphere and I look forward to attending the next one.

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