Literature review of methods for representing uncertainty

Abstract

This document provides a critical review of different frameworks for uncertainty analysis, in a risk analysis context: classical probabilistic analysis, imprecise probability (interval analysis), probability bound analysis, evidence theory, and possibility theory. The driver of the critical analysis is the decision-making process and the need to feed it with representative information derived from the risk assessment, to robustly support the decision. Technical details of the different frameworks are exposed only to the extent necessary to analyze and judge how these contribute to the communication of risk and the representation of the associated uncertainties to decision-makers, in the typical settings of high-consequence risk analysis of complex systems with limited knowledge on their behaviour.

An approach to quantify parameter uncertainty in early assessment of novel health technologies

“…This ignorance is problematic from both regulatory and reimbursement perspectives, as it may result in technologies with an unfavorable benefit‐risk profile entering the market (regulatory decision) or in products that are not cost‐effective being reimbursed (reimbursement decision). Compared to PSA, PBA requires fewer assumptions of the input parameters, that is, on their distribution, but it gives the same answer as PSA if the information is abundant enough (Zio & Pedroni, 2013). Probability bound analysis is especially advantageous for the assessment of medical technologies, where the uncertainty in the decision‐making on regulatory but also on reimbursement stage is very high, that is, the area between the two probability bounds is very large.…”