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

TL;DR

vop_poc_nz is a Python framework that enables comprehensive distributional cost-effectiveness analysis across multiple perspectives, helping decision-makers quantify the impact of perspective choice uncertainty in health economic evaluations.

Contribution

It introduces a novel Python package that allows simultaneous evaluation of multiple perspectives and quantifies perspective uncertainty using the Value of Perspective metric.

Findings

Demonstrated the framework with case studies from New Zealand.

Enabled quantification of opportunity costs due to perspective misalignment.

Provided tools for probabilistic sensitivity and equity impact analysis.

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.