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Conditional Poisson regression models are used in the SCCS design to estimate an incidence rate ratio (IRR), defined as the ratio of the incidence rate in the risk period to the incidence rate in the unexposed control period(s).
While these models perform well with large or moderate case samples, adverse events are rare in most vaccine safety studies, and maximum likelihood estimates (MLEs) may be biased (9, 10).
The self-controlled case series (SCCS) method is a study design for investigating the association between a transient exposure and an adverse event.
In this study, we evaluated the performance of Firth's bias-prevention method and the CM bias-correction method for correcting MLE bias in studies using the SCCS design with rare adverse events.
IRRs compare the incidence rate of adverse events in the risk period with the rate in the control period(s).
The IRRs are estimated using conditional Poisson regression models, conditioning on the number of events and the exposure history experienced by each individual during a predetermined observation period. The MLEs of α and β can be obtained using standard statistical software such as STATA (17) and SAS (18).
However, in some vaccine safety studies, the adverse events studied are rare and the maximum likelihood estimates may be biased.
Several bias correction methods have been examined in case-control studies using conditional logistic regression, but none of these methods have been evaluated in studies using the SCCS design.