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.

Video abstract

Supplementary file 6 (MP4 20900 kb)

中文翻译：

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方法论说明：报告马尔可夫、分区生存和其他非线性模型的确定性与概率结果

摘要

在不确定情况下做出决策时，选择导致最高预期净收益的路径是合理的。因此，为了为决策提供信息，基于决策模型的卫生经济评估应始终呈现预期输出（即与每个行动方案相关的平均成本和结果）。在诸如马尔可夫模型之类的非线性模型中，每个输入均值（确定性分析）的模型的单次“运行”将不会生成输出的预期值。在最坏的情况下，将确定性分析作为基本情况可能会导致误导性建议。因此，非线性模型的基本案例分析应该始终是概率分析的平均值。在本文中，我解释了为什么会这样，并为报告基于马尔可夫模型的经济评估提供建议，并指出相同的原则适用于其他非线性结构，例如分区生存模型和个体抽样模型。我还提供了对此类模型进行单向敏感性分析的建议。Microsoft Excel 和 R 中提供了说明示例的代码，以及电子补充材料中的视频摘要和用户指南。

视频摘要

补充文件 6 (MP4 20900 kb)