  The assessment of safety is an important aspect of the evaluation of new
therapies in clinical trials, with analyses of adverse events being an
essential part of this. Standard methods for the analysis of adverse events
such as the incidence proportion, i.e. the number of patients with a specific
adverse event out of all patients in the treatment groups, do not account for
both varying follow-up times and competing risks. Alternative approaches such
as the Aalen-Johansen estimator of the cumulative incidence function have been
suggested. Theoretical arguments and numerical evaluations support the
application of these more advanced methodology, but as yet there is to our
knowledge only insufficient empirical evidence whether these methods would lead
to different conclusions in safety evaluations. The Survival analysis for
AdVerse events with VarYing follow-up times (SAVVY) project strives to close
this gap in evidence by conducting a meta-analytical study to assess the impact
of the methodology on the conclusion of the safety assessment empirically. Here
we present the rationale and statistical concept of the empirical study
conducted as part of the SAVVY project. The statistical methods are presented
in unified notation and examples of their implementation in R and SAS are
provided.
