Iskandar, Rowan; Federici, Carlo; Berns, Cassandra; Blankart, Rudolf (2022). An approach to quantify parameter uncertainty in early assessment of novel health technologies. Health economics, 31 Suppl 1, pp. 116-134. Wiley 10.1002/hec.4525
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Iskandar_HealthEcon_2022.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (1MB) |
Health economic modeling of novel technology at the early stages of a product lifecycle has been used to identify technologies that are likely to be cost-effective. Such early assessments are challenging due to the potentially limited amount of data. Modelers typically conduct uncertainty analyses to evaluate their effect on decision-relevant outcomes. Current approaches, however, are limited in their scope of application and imposes an unverifiable assumption, that is, uncertainty can be precisely represented by a probability distribution. In the absence of reliable data, an approach that uses the fewest number of assumptions is desirable. This study introduces a generalized approach for quantifying parameter uncertainty, that is, probability bound analysis (PBA), that does not require a precise specification of a probability distribution in the context of early-stage health economic modeling. We introduce the concept of a probability box (p-box) as a measure of uncertainty without necessitating a precise probability distribution. We provide formulas for a p-box given data on summary statistics of a parameter. We describe an approach to propagate p-boxes into a model and provide step-by-step guidance on how to implement PBA. We conduct a case and examine the differences between the status-quo and PBA approaches and their potential implications on decision-making.
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