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vop_poc_nz: A Python Framework for Distributional Cost-Effectiveness and Value of Perspective Analysis

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  • Dylan A Mordaunt

Abstract

Health economic evaluations are sensitive to the choice of analytical perspective (e.g., health system vs. societal). While guidelines often recommend specific perspectives, the uncertainty associated with this choice - and the potential decision discordance it creates - is rarely quantified. We present vop_poc_nz, a Python package that implements a framework for Distributional Cost-Effectiveness Analysis (DCEA) and operationalizes the quantification of perspective uncertainty through the Value of Perspective (VoP) metric. The package provides tools for Markov modeling, probabilistic sensitivity analysis, value of information analysis, and equity impact assessment. Unlike existing tools that treat perspective as a fixed input, vop_poc_nz allows for the simultaneous evaluation of multiple perspectives. This enables decision-makers to estimate the opportunity cost of perspective misalignment. We demonstrate the package's capabilities using case studies from Aotearoa New Zealand.

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  • Dylan A Mordaunt, 2025. "vop_poc_nz: A Python Framework for Distributional Cost-Effectiveness and Value of Perspective Analysis," Papers 2512.03596, arXiv.org.
    • Handle: RePEc:arx:papers:2512.03596
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    1. Atkinson, Anthony B., 1970. "On the measurement of inequality," Journal of Economic Theory, Elsevier, vol. 2(3), pages 244-263, September.
    2. Susan Griffin & Nicky J. Welton & Karl Claxton, 2010. "Exploring the Research Decision Space: The Expected Value of Information for Sequential Research Designs," Medical Decision Making, , vol. 30(2), pages 155-162, March.
    3. Drummond, Michael F. & Sculpher, Mark J. & Claxton, Karl & Stoddart, Greg L. & Torrance, George W., 2015. "Methods for the Economic Evaluation of Health Care Programmes," OUP Catalogue, Oxford University Press, edition 4, number 9780199665884.
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