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

Rowan Iskandar, Carlo Federici, Cassandra Berns and Carl Blankart

Health Economics, 2022, vol. 31, issue S1, 116-134

Abstract: 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.

Date: 2022
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https://doi.org/10.1002/hec.4525

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Persistent link: https://EconPapers.repec.org/RePEc:wly:hlthec:v:31:y:2022:i:s1:p:116-134

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