Decisions about health interventions are often made using limited evidence. Mathematical models used to inform such decisions often include uncertainty analysis to account for the effect of uncertainty in the current evidence-base on decisional-relevant quantities. However, current uncertainty quantification methodologies require modelers to specify a precise probability distribution to represent the uncertainty of a model parameter. This study introduces a novel approach for propagating parameter uncertainty, probability bounds analysis (PBA), where the uncertainty about the unknown probability distribution of a model parameter is expressed in terms of an interval bounded by lower and upper bounds on the cumulative distribution function (p-box) and without assuming a particular form of the distribution function. We give the formulas of the p-boxes for common situations (given combinations of data on minimum, maximum, median, mean, or standard deviation), describe an approach to propagate p-boxes into a black-box mathematical model, and introduce an approach for decision-making based on the results of PBA. Then, we demonstrate an application of PBA using a case study. In sum, this study will provide modelers with tools to conduct parameter uncertainty quantification given constraints of available data with the fewest number of assumptions.
翻译:用于指导此类决定的数学模型往往包括不确定性分析,以说明当前证据基础中不确定性对决策相关数量的影响;然而,目前的不确定性量化方法要求模型家指定准确的概率分布,以代表模型参数的不确定性。本研究引入了一种传播参数不确定性的新办法,即概率约束分析(PBA),其中模型参数的未知概率分布的不确定性以累积分布功能(p-box)的下限和上限的间隔为表示,而没有假定分配功能的特定形式。我们给出了用于常见情况的p-box公式(提供最低、最高、中位、中位、平均或标准偏差的数据组合),描述了将p-box推广到黑箱数学模型的方法,并引入了基于PBA结果的决策方法。然后,我们用案例研究展示了PBA的应用情况。总而言,本研究将为模型提供工具,根据现有数据数量不多的假设进行参数不确定性量化。