Impact of Record-Linkage Errors in Covid-19 Vaccine-Safety Analyses using German Health-Care Data: A Simulation Study

Abstract

With unprecedented speed, 192,248,678 doses of Covid-19 vaccines were administered in Germany by July 11, 2023 to combat the pandemic. Limitations of clinical trials imply that the safety profile of these vaccines is not fully known before marketing. However, routine health-care data can help address these issues. Despite the high proportion of insured people, the analysis of vaccination-related data is challenging in Germany. Generally, the Covid-19 vaccination status and other health-care data are stored in separate databases, without persistent and database-independent person identifiers. Error-prone record-linkage techniques must be used to merge these databases. Our aim was to quantify the impact of record-linkage errors on the power and bias of different analysis methods designed to assess Covid-19 vaccine safety when using German health-care data with a Monte-Carlo simulation study. We used a discrete-time simulation and empirical data to generate realistic data with varying amounts of record-linkage errors. Afterwards, we analysed this data using a Cox model and the self-controlled case series (SCCS) method. Realistic proportions of random linkage errors only had little effect on the power of either method. The SCCS method produced unbiased results even with a high percentage of linkage errors, while the Cox model underestimated the true effect