  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.
