Quantifying the Value of Information in Early Stage Health Technology Assessment: Guiding Investment in Evidence Generation
Author(s)
Parampal Bajaj, B.Tech1, Akanksha Sharma, MSc1, Kushagra Pandey, MA1, Shubhram Pandey, MSc2.
1Heorlytics Pvt. Ltd, Mohali, India, 2Heorlytics Pvt. Ltd., SAS Nagar, Mohali, India.
1Heorlytics Pvt. Ltd, Mohali, India, 2Heorlytics Pvt. Ltd., SAS Nagar, Mohali, India.
OBJECTIVES: In early-stage health economic evaluations, the Expected Value of Perfect Information (EVPI) and Expected Value of Partial Perfect Information (EVPPI) serve as valuable tools for quantifying decision uncertainty. EVPI estimates the maximum potential benefit of completely eliminating uncertainty across all model parameters, while EVPPI focuses on the value of resolving uncertainty in specific subsets of parameters. The aim of this study was to illustrate the practical integration of EVPI and EVPPI within early economic modelling frameworks, highlighting their role in identifying critical drivers of uncertainty, informing research prioritization, and supporting go/no-go decisions for emerging health technologies.
METHODS: An early-stage decision-analytic model was developed to evaluate a novel intervention for recurrent or metastatic nasopharyngeal carcinoma. The model incorporated preliminary estimates for clinical efficacy, utilities, and cost inputs sourced from early-phase trials and expert opinion. To account for parameter uncertainty, a Probabilistic Sensitivity Analysis (PSA) was conducted by assigning probability distributions (e.g., beta for probabilities, gamma for costs) to all uncertain model parameters. The population-level EVPI was calculated and decomposed it into EVPPI for key parameter groups (efficacy, utilities, and costs).
RESULTS: The base-case analysis estimated an EVPI of $26,519 per patient, indicating that the value of eliminating decision uncertainty significantly outweighed the expected cost of additional research. EVPPI results highlighted health utility estimates as the most influential contributors to overall uncertainty, suggesting that targeted data collection in this area could yield the greatest benefit in improving decision confidence.
CONCLUSIONS: The application of EVPI and EVPPI in early stage economic modeling proved instrumental in identifying key areas of uncertainty and guiding evidence generation strategies. These value of information measures offer a systematic approach for optimizing resource allocation and informing research priorities, ultimately supporting more efficient and evidence-driven decision-making in healthcare.
METHODS: An early-stage decision-analytic model was developed to evaluate a novel intervention for recurrent or metastatic nasopharyngeal carcinoma. The model incorporated preliminary estimates for clinical efficacy, utilities, and cost inputs sourced from early-phase trials and expert opinion. To account for parameter uncertainty, a Probabilistic Sensitivity Analysis (PSA) was conducted by assigning probability distributions (e.g., beta for probabilities, gamma for costs) to all uncertain model parameters. The population-level EVPI was calculated and decomposed it into EVPPI for key parameter groups (efficacy, utilities, and costs).
RESULTS: The base-case analysis estimated an EVPI of $26,519 per patient, indicating that the value of eliminating decision uncertainty significantly outweighed the expected cost of additional research. EVPPI results highlighted health utility estimates as the most influential contributors to overall uncertainty, suggesting that targeted data collection in this area could yield the greatest benefit in improving decision confidence.
CONCLUSIONS: The application of EVPI and EVPPI in early stage economic modeling proved instrumental in identifying key areas of uncertainty and guiding evidence generation strategies. These value of information measures offer a systematic approach for optimizing resource allocation and informing research priorities, ultimately supporting more efficient and evidence-driven decision-making in healthcare.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
EE632
Topic
Economic Evaluation
Topic Subcategory
Value of Information
Disease
Oncology