Methodological Note: Reporting Deterministic versus Probabilistic Results of Markov, Partitioned Survival and Other...

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. Supplementary file 6 (MP4 20900 kb).