Survival analysis for AdVerse events with VarYing follow-up times (SAVVY) -- comparison of adverse event risks in randomized controlled trials

TL;DR

This paper demonstrates that using survival analysis techniques like the Aalen-Johansen estimator improves the accuracy of adverse event risk comparisons in clinical trials with varying follow-up times, censoring, and competing events.

Contribution

It provides a comprehensive comparison of estimators for adverse event risks, emphasizing the importance of the Aalen-Johansen estimator over traditional methods in clinical trial analysis.

Findings

Choice of estimator significantly affects bias in AE risk comparison.

Aalen-Johansen estimator outperforms Kaplan-Meier in bias reduction.

Hazard-based hazard ratios from Cox regression are preferable to incidence density ratios.

Abstract

Analyses of adverse events (AEs) are an important aspect of the evaluation of experimental therapies. The SAVVY (Survival analysis for AdVerse events with Varying follow-up times) project aims to improve the analyses of AE data in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times, censoring, and competing events (CE). In an empirical study including seventeen randomized clinical trials the effect of varying follow-up times, censoring, and competing events on comparisons of two treatment arms with respect to AE risks is investigated. The comparisons of relative risks (RR) of standard probability-based estimators to the gold-standard Aalen-Johansen estimator or hazard-based estimators to an estimated hazard ratio (HR) from Cox regression are done descriptively, with graphical displays, and using a random effects meta-analysis on AE level. The influence of different factors on the size of the bias is investigated in a meta-regression. We find 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 RR. There is an urgent need to improve the guidelines of reporting AEs so that incidence proportions are finally replaced by the Aalen-Johansen estimator - rather than by Kaplan-Meier - with appropriate definition of CEs. For RRs based on hazards, the HR based on Cox regression has better properties than the ratio of incidence densities.