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)

Thesis Thursday: Logan Trenaman

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Logan Trenaman who has a PhD from the University of British Columbia. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Economic evaluation of interventions to support shared decision-making: an extension of the valuation framework
Supervisors
Nick Bansback, Stirling Bryan
Repository link
http://hdl.handle.net/2429/66769

What is shared decision-making?

Shared decision-making is a process whereby patients and health care providers work together to make decisions. For most health care decisions, where there is no ‘best’ option, the most appropriate course of action depends on the clinical evidence and the patient’s informed preferences. In effect, shared decision-making is about reducing information asymmetry, by allowing providers to inform patients about the potential benefits and harms of alternative tests or treatments, and patients to express their preferences to their provider. The goal is to reach agreement on the most appropriate decision for that patient.

My thesis focused on individuals with advanced osteoarthritis who were considering whether to undergo total hip or knee replacement, or use non-surgical treatments such as pain medication, exercise, or mobility aids. Joint replacement alleviates pain and improves mobility for most patients, however, as many as 20-30% of recipients have reported insignificant improvement in symptoms and/or dissatisfaction with results. Shared decision-making can help ensure that those considering joint replacement are aware of alternative treatments and have realistic expectations about the potential benefits and harms of each option.

There are different types of interventions available to help support shared decision-making, some of which target the patient (e.g. patient decision aids) and some of which target providers (e.g. skills training). My thesis focused on a randomized controlled trial that evaluated a pre-consultation patient decision aid, which generated a summary report for the surgeon that outlined the patient’s knowledge, values, and preferences.

How can the use of decision aids influence health care costs?

The use of patient decision aids can impact health care costs in several ways. Some patient decision aids, such as those evaluated in my thesis, are designed for use by patients in preparation for a consultation where a treatment decision is made. Others are designed to be used during the consultation with the provider. There is some evidence that decision aids may increase up-front costs, by increasing the length of consultations, requiring investments to integrate decision aids into routine care, or train clinicians. These interventions may impact downstream costs by influencing treatment decision-making. For example, the Cochrane review of patient decision aids found that, across 18 studies in major elective surgery, those exposed to decision aids were less likely to choose surgery compared to those in usual care (RR: 0.86, 95% CI: 0.75 to 1.00).

This was observed in the trial-based economic evaluation which constituted the first chapter of my thesis. This analysis found that decision aids were highly cost-effective, largely due to a smaller proportion of patients undergoing joint replacement. Of course, this conclusion could change over time. One of the challenges of previous cost-effectiveness analysis (CEA) of patient decision aids has been a lack of long-term follow-up. Patients who choose not to have surgery over the short-term may go on to have surgery later. To look at the longer-term impact of decision aids, the third chapter of my thesis linked trial participants to administrative data with an average of 7-years follow-up. I found that, from a resource use perspective, the conclusion was the same as observed during the trial: fewer patients exposed to decision aids had undergone surgery, resulting in lower costs.

What is it about shared decision-making that patients value?

On the whole, the evidence suggests that patients value being informed, listened to, and offered the opportunity to participate in decision-making (should they wish!). To better understand how much shared decision-making is valued, I performed a systematic review of discrete choice experiments (DCEs) that had valued elements of shared decision-making. This review found that survey respondents (primarily patients) were willing to wait longer, pay, and in some cases willing to accept poorer health outcomes for greater shared decision-making.

It is important to consider preference heterogeneity in this context. The last chapter of my PhD performed a DCE to value shared decision-making in the context of advanced knee osteoarthritis. The DCE included three attributes: waiting time, health outcomes, and shared decision-making. The latent class analysis found four distinct subgroups of patients. Two groups were balanced, and traded between all attributes, while one group had a strong preference for shared decision-making, and another had a strong preference for better health outcomes. One important finding from this analysis was that having a strong preference for shared decision-making was not associated with demographic or clinical characteristics. This highlights the importance of each clinical encounter in determining the appropriate level of shared decision-making for each patient.

Is it meaningful to estimate the cost-per-QALY of shared decision-making interventions?

One of the challenges of my thesis was grappling with the potential conflict between the objectives of CEA using QALYs (maximizing health) and shared decision-making interventions (improved decision-making). Importantly, encouraging shared decision-making may result in patients choosing alternatives that do not maximize QALYs. For example, informed patients may choose to delay or forego elective surgery due to potential risks, despite it providing more QALYs (on average).

In cases where a CEA finds that shared decision-making interventions result in poorer health outcomes at lower cost, I think this is perfectly acceptable (provided patients are making informed choices). However, it becomes more complicated when shared decision-making interventions increase costs, result in poorer health outcomes, but provide other, non-health benefits such as informing patients or involving them in treatment decisions. In such cases, decision-makers need to consider whether it is justified to allocate scarce health care resources to encourage shared decision-making when it requires sacrificing health outcomes elsewhere. The latter part of my thesis tried to inform this trade-off, by valuing the non-health benefits of shared decision-making which would not otherwise be captured in a CEA that uses QALYs.

How should the valuation framework be extended, and is this likely to indicate different decisions?

I extended the valuation framework by attempting to value non-health benefits of shared decision-making. I followed guidelines from the Canadian Agency for Drugs and Technologies in Health, which state that “the value of non-health effects should be based on being traded off against health” and that societal preferences be used for this valuation. Requiring non-health benefits to be valued relative to health reflects the opportunity cost of allocating resources toward these outcomes. While these guidelines do not specifically state how to do so, I chose to value shared decision-making relative to life-years using a chained (or two-stage) valuation approach so that they could be incorporated within the QALY.

Ultimately, I found that the value of the process of shared decision-making was small, however, this may have an impact on cost-effectiveness. The reasons for this are twofold. First, there are few cases where shared decision-making interventions improve health outcomes. A 2018 sub-analysis of the Cochrane review of patient decision aids found little evidence that they impact health-related quality of life. Secondly, the up-front cost of implementing shared decision-making interventions may be small. Thus, in cases where shared decision-making interventions require a small investment but provide no health benefit, the non-health value of shared decision-making may impact cost-effectiveness. One recent example from Dr Victoria Brennan found that incorporating process utility associated with improved consultation quality, resulting from a new online assessment tool, increased the probability that the intervention was cost-effective from 35% to 60%.