  The SAVVY project aims to improve the analyses of adverse events (AEs) in
clinical trials through the use of survival techniques appropriately dealing
with varying follow-up times and competing events (CEs). This paper summarizes
key features and conclusions from the various SAVVY papers. Through theoretical
investigations using simulations and in an empirical study including randomized
clinical trials from several sponsor organisations, biases from ignoring
varying follow-up times or CEs are investigated. The bias of commonly used
estimators of the absolute and relative AE risk is quantified. Furthermore, we
provide a cursory assessment of how pertinent guidelines for the analysis of
safety data deal with the features of varying follow-up time and CEs. SAVVY
finds that for both, avoiding bias and categorization of evidence with respect
to treatment effect on AE risk into categories, the choice of the estimator is
key and more important than features of the underlying data such as percentage
of censoring, CEs, amount of follow-up, or value of the gold-standard. The
choice of the estimator of the cumulative AE probability and the definition of
CEs are crucial. SAVVY recommends using the Aalen-Johansen estimator (AJE) with
an appropriate definition of CEs whenever the risk for AEs is to be quantified.
There is an urgent need to improve the guidelines of reporting AEs so that
incidence proportions or one minus Kaplan-Meier estimators are finally replaced
by the AJE with appropriate definition of CEs.
