Measuring sampling plan utility in post-marketing surveillance of medical products

Abstract

Ensuring product quality is critical to combating the global challenge of substandard and falsified medical products. Post-marketing surveillance is a central quality-assurance activity in which products from consumer-facing locations are collected and tested. Regulators in low-resource settings use post-marketing surveillance to evaluate product quality across locations and determine corrective actions. Part of post-marketing surveillance is developing a sampling plan, which specifies where to test and the number of tests to conduct at a location. With limited resources, it is important to base decisions on the utility of the samples tested. We propose a Bayesian approach to generate a comprehensive utility metric for sampling plans. This sampling plan utility integrates regulatory risk assessments with prior testing data, available supply-chain information, and valuations of regulatory objectives. We develop an efficient method for calculating sampling plan utility. We illustrate the value of the utility metric with a case study based on de-identified post-marketing surveillance data from a low-resource setting.