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A modified self‐controlled case series method for event‐dependent exposures and high event‐related mortality, with application to COVID‐19 vaccine safety
Author(s) -
GhebremichaelWeldeselassie Yonas,
Jabagi Marie Joëlle,
Botton Jérémie,
Bertrand Marion,
Baricault Bérangère,
Drouin Jérôme,
Weill Alain,
Zureik Mahmoud,
DraySpira Rosemary,
Farrington Paddy
Publication year - 2022
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9325
Subject(s) - event (particle physics) , covid-19 , series (stratigraphy) , computer science , medicine , econometrics , statistics , virology , mathematics , disease , biology , outbreak , paleontology , physics , quantum mechanics , infectious disease (medical specialty)
We propose a modified self‐controlled case series (SCCS) method to handle both event‐dependent exposures and high event‐related mortality. This development is motivated by an epidemiological study undertaken in France to quantify potential risks of cardiovascular events associated with COVID‐19 vaccines. Event‐dependence of vaccinations, and high event‐related mortality, are likely to arise in other SCCS studies of COVID‐19 vaccine safety. Using this case study and simulations to broaden its scope, we explore these features and the biases they may generate, implement the modified SCCS model, illustrate some of the properties of this model, and develop a new test for presence of a dose effect. The model we propose has wider application, notably when the event of interest is death.