ESTIMATING DEMAND FOR PHARMACEUTICAL PRODUCTS UNDER INFORMATION LIMITATIONS

Abstract

This paper addresses the challenge of assessing pharmaceutical product demand in the presence of incomplete, censored, and partially observed data, which are common in real-world healthcare markets due to supply constraints, regulatory restrictions, and patient behavior. Traditional econometric approaches often focus on post-estimation diagnostics and statistical correctness but fail to ensure that demand estimates can be reliably used for economically sound decision-making in portfolio management, pricing, and reimbursement negotiations. To address this gap, we introduce the Decision Readiness Index (DRI), a deterministic, modular indicator designed to evaluate the adequacy of available information before applying any modeling or optimization procedures. DRI aggregates key data quality metrics—including completeness, accuracy, coverage, and temporal consistency—into a single signal that functions as a preliminary filter of acceptability, independent of the specific optimization or econometric method. This modularity allows decision-makers to standardize the evaluation process while retaining the flexibility to apply various analytical techniques without compromising economic validity. By implementing DRI, firms can identify whether information is sufficient to support decisions regarding pricing, product launches, and portfolio allocation, mitigating the risk of economically costly errors caused by biased or incomplete data. Furthermore, the proposed approach operationalizes concepts from stochastic programming and the value-of-information literature, emphasizing that the usefulness of data is determined not only by statistical properties but also by its ability to inform decisions under uncertainty. The framework extends current methodologies by integrating partial identification, censoring corrections, and measurement error adjustments, providing a practical tool for pre-estimation assessment in pharmaceutical demand analysis. Overall, this study contributes to both theory and practice by offering a structured mechanism to evaluate the readiness of data, thereby supporting robust, economically grounded decisions in the management of pharmaceutical portfolios, pricing strategies, and reimbursement policies. The findings also open avenues for further research on integrating DRI with intelligent decision-support platforms and expanding its applicability to dynamic and multi-channel market environments.