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Methodological Note: Reporting Deterministic versus Probabilistic Results of Markov, Partitioned Survival and Other Non-Linear Models

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Abstract

When making decisions under uncertainty, it is reasonable to choose the path that leads to the highest expected net benefit. Therefore, to inform decision making, decision-model-based health economic evaluations should always present expected outputs (i.e. the mean costs and outcomes associated with each course of action). In non-linear models such as Markov models, a single ‘run’ of the model with each input at its mean (a deterministic analysis) will not generate the expected value of the outputs. In a worst-case scenario, presenting deterministic analyses as the base case can lead to misleading recommendations. Therefore, the base-case analysis of a non-linear model should always be the means from a probabilistic analysis. In this paper, I explain why this is the case and provide recommendations for reporting economic evaluations based on Markov models, noting that the same principle applies to other non-linear structures such as partitioned survival models and individual sampling models. I also provide recommendations for conducting one-way sensitivity analyses of such models. Code illustrating the examples is provided in both Microsoft Excel and R, along with a video abstract and user guides in the electronic supplementary material.

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Correspondence to Edward C. F. Wilson.

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Edward CF Wilson has no conflicts of interest that are directly relevant to the content of this article.

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ECFW was solely responsible for all aspects of creating this manuscript.

Supplementary Information

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Supplementary file 1 (DOCX 31 kb)

Supplementary file 2 (R 5 kb)

Supplementary file 3 (XLSM 7068 kb)

Supplementary file 4 (MP4 72022 kb)

Supplementary file 5 (MP4 42802 kb)

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Wilson, E.C.F. Methodological Note: Reporting Deterministic versus Probabilistic Results of Markov, Partitioned Survival and Other Non-Linear Models. Appl Health Econ Health Policy 19, 789–795 (2021). https://doi.org/10.1007/s40258-021-00664-2

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