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Modelling the Cost-Effectiveness of Diagnostic Tests

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Abstract

Diagnostic tests are used to determine whether a disease or condition is present or absent in a patient, who will typically be suspected of having the disease or condition due to symptoms or clinical signs. Economic evaluations of diagnostic tests (e.g. cost-effectiveness analyses) can be used to determine whether a test produces sufficient benefit to justify its cost. Evidence on the benefits conferred by a test is often restricted to its accuracy, which means mathematical models are required to estimate the impact of a test on outcomes that matter to patients and health payers. It is important to realise the case for introducing a new test may not be restricted to its accuracy, but extend to factors such as time to diagnosis and acceptability for patients. These and other considerations may mean the common modelling approach, the decision tree, is inappropriate for underpinning an economic evaluation. There are no consensus guidelines on how economic evaluations of diagnostic tests should be conducted—this article attempts to explore the common challenges encountered in economic evaluations, suggests solutions to those challenges, and identifies some areas where further methodological work may be necessary.

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Acknowledgements

I thank Prof. Chris Hyde (University of Exeter) for reviewing a draft of this manuscript, which prompted valuable discussion.

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Authors and Affiliations

  1. Health Economics Group, University of Exeter, Exeter, UK

    Tristan Snowsill PhD

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  1. Tristan Snowsill PhD
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Correspondence to Tristan Snowsill PhD.

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The author declares no conflict of interest.

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TS is the sole author.

Data availability

There are no data included in this article that are not in the public domain. The simulated example shown in Fig. 2 can be replicated by drawing \(\left({X}_{1},{X}_{2}\right)\) from a bivariate normal distribution with mean \(\left(\mathrm{0,0}\right)\) and covariance matrix \(\left(\begin{array}{cc}1& 0.4\\ 0.4& 1\end{array}\right)\), then simulating \({Z}_{1}\sim \mathcal{N}\left({X}_{1},0.2\right),{Z}_{2}\sim \mathcal{N}\left({X}_{1},0.2\right)\) and \({Z}_{3}\sim \mathcal{N}\left({X}_{2},0.2\right)\), and finally simulating the test results as \({Y}_{1}=I\left({Z}_{1}>0.6\right)\), \({Y}_{2}=I\left({Z}_{2}>0\right)\) and \({Y}_{3}=I\left({Z}_{3}>0.5\right)\). The true disease status is given by \(I\left({X}_{1}+{X}_{2}>1\right)\).

Supplementary Information

Below is the link to the electronic supplementary material.

40273_2023_1241_MOESM1_ESM.xlsx

Electronic Supplementary Material: Example decision tree implemented in Excel showing the importance of accounting for heterogeneity in pre-test probability. (XLSX 111 KB)

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Snowsill, T. Modelling the Cost-Effectiveness of Diagnostic Tests. PharmacoEconomics 41, 339–351 (2023). https://doi.org/10.1007/s40273-023-01241-2

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