2 research outputs found
Impact of Record-Linkage Errors in Covid-19 Vaccine-Safety Analyses using German Health-Care Data: A Simulation Study
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
Static and Dynamic Accuracy and Occlusion Robustness of SteamVR Tracking 2.0 in Multi-Base Station Setups
The tracking of objects and person position, orientation, and movement is relevant for various medical use cases, e.g., practical training of medical staff or patient rehabilitation. However, these demand high tracking accuracy and occlusion robustness. Expensive professional tracking systems fulfill these demands, however, cost-efficient and potentially adequate alternatives can be found in the gaming industry, e.g., SteamVR Tracking. This work presents an evaluation of SteamVR Tracking in its latest version 2.0 in two experimental setups, involving two and four base stations. Tracking accuracy, both static and dynamic, and occlusion robustness are investigated using a VIVE Tracker (3.0). A dynamic analysis further compares three different velocities. An error evaluation is performed using a Universal Robots UR10 robotic arm as ground-truth system under nonlaboratory conditions. Results are presented using the Root Mean Square Error. For static experiments, tracking errors in the submillimeter and subdegree range are achieved by both setups. Dynamic experiments achieved errors in the submillimeter range as well, yet tracking accuracy suffers from increasing velocity. Four base stations enable generally higher accuracy and robustness, especially in the dynamic experiments. Both setups enable adequate accuracy for diverse medical use cases. However, use cases demanding very high accuracy should primarily rely on SteamVR Tracking 2.0 with four base stations