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Forget Blue Monday, let’s energise ourselves with some exciting research from the December issue of PharmacoEconomics. I’ll focus on the four papers about cost-effectiveness analysis of diagnostic tests, because this is where I’ve been conducting some recent work.
Cost-effectiveness analysis of diagnostic tests can be more complex than therapeutic technologies because the tests’ value is often driven by the benefits of treatment decisions, which tests inform, rather than by their direct effects on people’s health. This means that a cost-effectiveness analysis may need to link the test accuracy in detecting a condition to the benefits of the correct treatment. If you’d like to know more, read my post about cost-effectiveness analysis of diagnostic tests from a couple of years ago.
Simon van der Pol and colleagues have two papers on diagnostic tests. One is a systematic review of economic evaluations of diagnostic techniques for respiratory tract infections that gives some insights into the design and practice of these sorts of studies. The authors included 70 papers, mostly modelling studies, generally using a decision tree structure. Decision trees allow for the comparison of many ways to use diagnostic tests whilst being computationally quite simple. Long-term outcomes can be calculated using another model (or module) and used as inputs to the decision tree. Interestingly, 7 studies used a dynamic model to account for the benefits of the diagnostic test in terms of reduced transmission, and 20 considered antimicrobial resistance. Given the variation in the outcomes and in light of the new EU in-vitro diagnostic regulation law, the authors suggested that companies include quality-of-life measurements in the trials of diagnostic tests and collect sufficient clinical outcomes to allow for the extrapolation to longer time horizons – companies, please take note!
Informed by this review, Simon and colleagues identified some issues in the conduct and reporting of economic evaluations of diagnostic tests and proposed 8 recommendations to address them. For example, some recommendations are about how to specify the target population, the setting and location, all of which affect the prevalence of the disease, hence the cost-effectiveness of the test. There are also recommendations about comparators, time horizon, choice of health outcomes, estimating resources and costs, incremental analysis, and the value assessment for affordability and reimbursement. These recommendations align well with good practice in economic evaluations in general, and will surely be a useful resource for analysts.
If you would like to learn about how to do a cost-effectiveness analysis of companion diagnostic tests in R, Mikyung Kelly Seo and Mark Strong published a must-read tutorial. Companion tests are tests that provide information on whether individuals have a biomarker that indicates that the disease is sensitive to targeted therapies, such as tests to determine whether a tumour has a specific genetic mutation. The paper is about building a state-transition model for the decision problem of whether to test and treat individuals with biomarker-guided therapy, to treat all individuals with the targeted therapy (without testing them), or to treat all individuals with standard care. These three strategies are in line with many cost-effectiveness analyses conducted to inform reimbursement decisions. The model is a Markov model with three health states: pre-progression, post-progression, and death, which is a typical structure for cancer models. The paper goes through the steps of coding the model in R, starting with creating transition matrices, creating the matrices with costs and utility inputs, building the model trace, computing outcomes, and doing sensitivity analyses. This is a good reference paper for everyone who would like to learn how to use R in cost-effectiveness analysis.
Last but certainly not least, we have the paper by Martijn Simons and colleagues on a cost-effectiveness analysis of whole-genome sequencing for a type of lung cancer. Whole-genome sequencing is a diagnostic test that provides information on molecular targets for treatments. The downside is that it is expensive, and because it is not clear how many actionable targets can be found, the benefits are uncertain. This study is an early cost-effectiveness analysis comparing standard of care, where individuals are tested using the standard of care tests, which do not detect all targets; testing individuals with whole-genome sequencing; and testing individuals first with the standard of care tests, then in those in whom no targets were found, testing with whole-genome sequencing. The authors found that whole-genome sequencing resulted in greater health benefits, but also greater costs, and it is not cost-effective in the Dutch setting unless its price was lower and the prevalence of actionable targets found by this test was higher.
The other papers are on how to consider intersectionality in cost-effectiveness analysis, how to include benefits beyond health in the quality-adjusted life year, about a NICE technology appraisal on filgotinib for rheumatoid arthritis, on the importance of illness attributes that affect the value of alleviating that illness to inform resource allocation, and about a survey and workshop on the challenges of COVID-19 for health technology assessment agencies.