Survival analysis for AdVerse events with VarYing follow-up times (SAVVY): summary of findings and a roadmap for the future of safety analyses in clinical trials

Background 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. Methods 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. Results 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. Conclusions 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.