Non-health economists (henceforth referred to as ‘lay stakeholders’) are often asked to use the outputs of cost-effectiveness models to inform decisions, but they can find them difficult to understand. Conversely, health economists may have limited experience of explaining cost-effectiveness models to lay stakeholders. How can we do better?
This article shares my experience of explaining cost-effectiveness models of diagnostic tests to lay stakeholders such as researchers in other fields, clinicians, managers, and patients, and suggests some approaches to make models easier to understand. It is the condensed version of my presentation at ISPOR Europe 2018.
Why are cost-effectiveness models of diagnostic tests difficult to understand?
Models designed to compare diagnostic strategies are particularly challenging. In my view, this is for two reasons.
Firstly, there is the sheer number of possible diagnostic strategies that a cost-effectiveness model allows us to compare. Even if we are looking at only a couple of tests, we can use them in various combinations and at many diagnostic thresholds. See, for example, this cost-effectiveness analysis of diagnosis of prostate cancer.
Secondly, diagnostic tests can affect costs and health outcomes in multiple ways. Specifically, diagnostic tests can have a direct effect on people’s health-related quality of life, mortality risk, acquisition costs, as well as the consequences of side effects. Furthermore, diagnostic tests can have an indirect effect via the consequences of the subsequent management decisions. This indirect effect is often the key driver of cost-effectiveness.
As a result, the cost-effectiveness analysis of diagnostic tests can have many strategies, with multiple effects modelled in the short and long-term. This makes the model and the results difficult to understand.
Map out the effect of the test on health outcomes or costs
The first step in developing any cost-effectiveness model is to understand how the new technology, such as a diagnostic test or a drug, can impact the patient and the health care system. Ferrante di Ruffano et al and Kip et al are two studies that can be used as a starting point to understand the possible effects of a test on health outcomes and/or costs.
Ferrante di Ruffano et al conducted a review of the mechanisms by which diagnostic tests can affect health outcomes and provides a list of the possible effects of diagnostic tests.
Kip et al suggests a checklist for the reporting of cost-effectiveness analyses of diagnostic tests and biomarkers. Although this is a checklist for the reporting of a cost-effectiveness analysis that has been previously conducted, it can also be used as a prompt to define the possible effects of a test.
Reach a shared understanding of the clinical pathway
The parallel step is to understand the clinical pathway in which the diagnostic strategies integrate and affect. This consists of conceptualising the elements of the health care service relevant for the decision problem. If you’d like to know more about model conceptualisation, I suggest this excellent paper by Paul Tappenden.
These conceptual models are necessarily simplifications of reality. They need to be as simple as possible, but accurate enough that lay stakeholders recognise it as valid. As Einstein said: “to make the irreducible basic elements as simple and as few as possible, without having to surrender the adequate representation of a single datum of experience.”
Agree which impacts to include in the cost-effectiveness model
What to include and to exclude from the model is, at present, more of an art than a science. For example, Chilcott et al conducted a series of interviews with health economists and found that their approach to model development varied widely.
I find that the best approach is to design the model in consultation with the relevant stakeholders, such as clinicians, patients, health care managers, etc. This ensures that the cost-effectiveness model has face validity to those who will ultimately be their end user and (hopefully) advocates of the results.
Decouple the model diagram from the mathematical model
When we have a reasonable idea of the model that we are going to build, we can draw its diagram. A model diagram not only is a recommended component of the reporting of a cost-effectiveness model but also helps lay stakeholders understand it.
The temptation is often to draw the model diagram as similar as possible to the mathematical model. In cost-effectiveness models of diagnostic tests, the mathematical model tends to be a decision tree. Therefore, we often see a decision tree diagram.
The problem is that decision trees can easily become unwieldy when we have various test combinations and decision nodes. We can try to synthesise a gigantic decision tree into a simpler diagram, but unless you have great graphic designer skills, it might be a futile exercise (see, for example, here).
An alternative approach is to decouple the model diagram from the mathematical model and break down the decision problem into steps. The figure below shows an example of how the model diagram can be decoupled from the mathematical model.
The diagram breaks the problem down into steps that relate to the clinical pathway, and therefore, to the stakeholders. In this example, the diagram follows the questions that clinicians and patients may ask: which test to do first? Given the result of the first test, should a second test be done? If a second test is done, which one?
Relate the results to the model diagram
The next point of contact between the health economists and lay stakeholders is likely to be at the point when the first cost-effectiveness results are available.
The typical chart for the probabilistic results is the cost-effectiveness acceptability curve (CEAC). In my experience, the CEAC is challenging for lay stakeholders. It plots results over a range of cost-effectiveness thresholds, which are not quantities that most people outside cost-effectiveness analysis relate to. Additionally, CEACs showing the results of multiple strategies can have many lines and some discontinuities, which can be difficult to understand by the untrained eye.
An alternative approach is to re-use the model diagram to present the results. The model diagram can show the strategy that is expected to be cost-effective and its probability of cost-effectiveness at the relevant threshold. For example, the probability that the strategies starting with a specific test are cost-effective is X%; and the probability that strategies using the specific test at a specific cut-off are cost-effective is Y%, etc.
Next steps for practice and research
Research about the communication of cost-effectiveness analysis is sparse, and guidance is lacking. Beyond the general advice to speak in plain English and avoiding jargon, there is little advice. Hence, health economists find themselves developing their own approaches and techniques.
In my experience, the key aspects for effective communication are to engage with lay stakeholders from the start of the model development, to explain the intuition behind the model in simplified diagrams, and to find a balance between scientific accuracy and clarity which is appropriate for the audience.
More research and guidance are clearly needed to develop communication methods that are effective and straightforward to use in applied cost-effectiveness analysis. Perhaps this is where patient and public involvement can really make a difference!