QUAIL: Quality Analyser for Interpreting Linkage - Maizey, Leah; Wray, Matt; Antal, Laszlo; Thomson, Gavin; Gammon, Tim; Linaker, Jack (International Journal of Population Data Science, 2025)
Objective The QUality Analyser for Interpreting Linkage (QUAIL) is a project that aims to provide a package of recommended researched methodologies and tools to streamline the quality assurance process of linked data. We aim for QUAIL to be as generalisable and as automated as possible. Standardised, yet suitable for a variety of linkage contexts. Methods We will highlight key processes and considerations made in the development of QUAIL and its sub-modules. These include candidate link creation for the purpose of false positive and false negative estimation, stratification, sampling, clerical review, and the computation of key quality metrics, including precision and recall. Results and Conclusion Results will focus on our recommendations based on our evaluation of each of these modules separately, and in conjunction. We will highlight the advantages of using QUAIL to facilitate quality assurance of linked data. This includes reducing reviewer burden with easy-to-use tools, while simultaneously enhancing the accuracy, reliability, efficiency and reproducibility of quality reporting.