from The Academic Health Economists’ Blo… at http://bit.ly/2SBLrCw on December 10, 2018 at 12:05PM
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.
Calculating the expected value of sample information using efficient nested Monte Carlo: a tutorial. Value in Health [PubMed] Published 17th July 2018
The expected value of sample information (EVSI) represents the added benefit from collecting new information on specific parameters in future studies. It can be compared to the cost of conducting these future studies to calculate the expected net benefit of sampling. The objective is to help inform which study design is best, given the information it can gather and its costs. The theory and methods to calculate EVSI have been around for some time, but we rarely see it in applied economic evaluations.
In this paper, Anna Heath and Gianluca Baio present a tutorial about how to implement a method they had previously published on, which is more computationally efficient than the standard nested Monte Carlo simulations.
The authors start by explaining the method in theory, then illustrate it with a simple worked example. I’ll admit that I got a bit lost with the theory, but I found that the example made it much clearer. They demonstrate the method’s performance using a previously published cost-effectiveness model. Additionally, they have very helpfully published a suite of functions to apply this method in practice.
I really enjoyed reading this paper, as it takes the reader step-by-step through the method. However, I wasn’t sure about when this method is applicable, given that the authors note that it requires a large number of probabilistic simulations to perform well, and it is only appropriate when EVPPI is high. The issue is, how large is large and how high is high? Hopefully, these and other practical questions are on the list for this brilliant research team.
As an applied researcher, I find tutorial papers such as this one incredibly useful to learn new methods and help implement them in practice. Thanks to work such as this one and others, we’re getting close to making value of information analysis a standard element of cost-effectiveness studies.
Future costs in cost-effectiveness analyses: past, present, future. PharmacoEconomics [PubMed] Published 26th November 2018
Linda de Vries, Pieter van Baal and Werner Brouwer help illuminate the debate on future costs with this fascinating paper. Future costs are the costs of resources used by patients during the years of life added by the technology under evaluation. Future costs can be distinguished between related or unrelated, depending on whether the resources are used for the target disease. They can also be distinguished between medical or non-medical, depending on whether the costs fall on the healthcare budget.
The authors very skilfully summarise the theoretical literature on the inclusion of future costs. They conclude that future related and unrelated medical costs should be included and present compelling arguments to do so.
They also discuss empirical research, such as studies that estimate future unrelated costs. The references are a useful starting point for other researchers. For example, I noted that there is a tool to include future unrelated medical costs in the Netherlands and some studies on their estimation in the UK (see, for example, here).
There is a thought-provoking section on ethical concerns. If unrelated costs are included, technologies that increase the life expectancy of people who need a lot of resources will look less cost-effective. The authors suggest that these issues should not be concealed in the analysis, but instead dealt with in the decision-making process.
This is an enjoyable paper that provides an overview of the literature on future costs. I highly recommend it to get up to speed with the arguments and the practical implications. There is clearly a case for including future costs, and the question now is whether the cost-effectiveness practice follows suit.
Cost-utility analysis using EQ-5D-5L data: does how the utilities are derived matter? Value in Health Published 4th July 2018
We’ve recently become spoilt for choice when it comes to the EQ-5D. To obtain utility values, just in the UK, there are a few options: the 3L tariff, the 5L tariff, and crosswalk tariffs by Ben van Hout and colleagues and Mónica Hernandez and colleagues [PDF]. Which one to choose? And does it make any difference?
Fan Yang and colleagues have done a good job in getting us closer to the answer. They estimated utilities obtained from EQ-5D-5L data using the 5L value set and crosswalk tariffs to EQ-5D-3L and tested the values in cost-effectiveness models of hemodialysis compared to peritoneal dialysis.
Reassuringly, hemodialysis had always greater utilities than peritoneal dialysis. However, the magnitude of the difference varied with the approach. Therefore, using either EQ-5D-5L or the crosswalk tariff to EQ-5D-3L can influence the cost-effectiveness results. These results are in line with earlier work by Mónica Hernandez and colleagues, who compared the EQ-5D-3L with the EQ-5D-5L.
The message is clear in that both the type of EQ-5D questionnaire and the EQ-5D tariff makes a difference to the cost-effectiveness results. This can have huge policy implications as decisions by HTA agencies, such as NICE, depend on these results.
Which EQ-5D-5L to use in a new primary research study remains an open question. In the meantime, NICE recommends the use of the EQ-5D-3L or, if EQ-5D-5L was collected, Ben van Hout and colleagues’ mapping function to the EQ-5D-3L. Hopefully, a definite answer won’t be long in coming.