Introduction to voiage

What is Value of Information (VOI) Analysis?

Value of Information (VOI) analysis is a quantitative decision analysis technique used to estimate the economic benefit of collecting additional information before making a decision under uncertainty. It helps answer questions like:

• “Is it worth investing in more research to reduce uncertainty about this decision?”
• “Which specific uncertainties, if resolved, would provide the most value?”
• “What is the maximum amount we should be willing to pay for a particular piece of research?”
• “Which proposed study design offers the best value for money?”

VOI is particularly prominent in health technology assessment (HTA), where decisions about adopting new medical treatments or technologies involve significant uncertainty and potentially large population health and budget impacts. However, its principles are applicable across many fields including environmental management, engineering, finance, and public policy.

Key VOI Metrics

Expected Value of Perfect Information (EVPI)

The expected increase in net benefit if all uncertainty about model parameters were eliminated. It represents the maximum value of any further research.

Expected Value of Partial Perfect Information (EVPPI)

The expected increase in net benefit if uncertainty about a specific subset of model parameters were eliminated. Useful for identifying key drivers of decision uncertainty.

Expected Value of Sample Information (EVSI)

The expected increase in net benefit from conducting a particular research study (e.g., a clinical trial of a specific design and sample size). This is often the most practical VOI metric for guiding research decisions.

Expected Net Benefit of Sampling (ENBS)

Calculated as EVSI minus the cost of the proposed research. A positive ENBS suggests the research is economically worthwhile.

Frontier Methods

voiage also tracks frontier and adjacent methods such as:

• Value of Perspective — valuing decision perspective shifts
• Distributional/Equity VOI — incorporating equity weights
• Implementation-Adjusted VOI — accounting for implementation barriers
• Dynamic Real-Options VOI — valuing flexibility in sequential decisions
• Causal VOI — VOI in causal inference settings
• Data-Quality VOI — valuing improvements in data quality
• Computational VOI — valuing computational model refinement
• Expert Elicitation VOI — valuing expert knowledge synthesis

Why voiage?

While VOI methods are well-established with implementations in R (e.g., BCEA, dampack, voi), a comprehensive, modern, and extensible multi-language library for VOI analysis is still a developing area. voiage aims to:

• Provide a user-friendly Python API for common and advanced VOI calculations
• Leverage the Python scientific computing ecosystem (NumPy, SciPy, pandas, xarray, NumPyro, JAX) for performance and flexibility
• Offer implementations for a wide range of VOI analyses, including frontier methods
• Support Rust-first bindings for Python/Mojo, R, and Julia
• Utilize a high-performance Rust execution core for compute-intensive paths
• Follow contract-first development with explicit schemas and deterministic fixtures

Target Audience

voiage is intended for:

• Health economists and HTA practitioners
• Decision analysts and operations researchers
• Statisticians involved in clinical trial design and Bayesian analysis
• Researchers in any field applying decision theory and uncertainty quantification
• Students learning about VOI methods

Getting Help

If you encounter any issues or have questions, please raise an issue on GitHub.