Survival analysis for AdVerse events with VarYing follow-up times (SAVVY): Rationale and statistical concept of a meta-analytic study

TL;DR

This paper discusses the rationale and statistical methodology for a meta-analytical study evaluating how advanced survival analysis methods impact safety assessments of adverse events in clinical trials with varying follow-up times.

Contribution

It introduces the SAVVY project, which empirically assesses the impact of advanced survival analysis methods on safety conclusions in clinical trials.

Findings

Supports use of Aalen-Johansen estimator over traditional methods

Provides R and SAS implementation examples

Highlights potential differences in safety conclusions

Abstract

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.