Probability bound analysis: A novel approach for quantifying parameter uncertainty in decision-analytic modeling and cost-effectiveness analysis

TL;DR

This paper introduces probability bounds analysis (PBA), a new method for quantifying parameter uncertainty in decision models without assuming specific probability distributions, enhancing decision-making under limited data.

Contribution

The study presents PBA as a novel approach that uses interval bounds on distributions, avoiding the need for precise probability assumptions in uncertainty analysis.

Findings

PBA effectively quantifies uncertainty with limited data.

PBA provides comparable results to PSA in case studies.

The method offers practical tools for decision-making under uncertainty.

Abstract

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 decision-relevant quantities. However, current uncertainty quantification methodologies, including probabilistic sensitivity analysis (PSA), 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 unknown 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. We demonstrate the characteristics and utility of PBA versus PSA using two case studies. In sum, this study provides modelers with practical tools to conduct parameter uncertainty quantification given the constraints of available data and with the fewest assumptions.