Survival analysis for AdVerse events with VarYing follow-up times (SAVVY) -- estimation of adverse event risks

TL;DR

This study evaluates survival analysis methods for adverse event data in clinical trials, highlighting biases from traditional estimators and advocating for the Aalen-Johansen estimator to improve risk estimation accuracy.

Contribution

It compares common AE analysis estimators with the Aalen-Johansen estimator, demonstrating the latter's superiority in handling censoring and competing events in clinical trial data.

Findings

KME overestimates AE risks by about 20%.

Bias is mainly due to censoring and competing events.

AJE provides more accurate AE risk estimates.

Abstract

The SAVVY project aims to improve the analyses of adverse event (AE) data in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events (CEs). Although statistical methodologies have advanced, in AE analyses often the incidence proportion, the incidence density, or a non-parametric Kaplan-Meier estimator (KME) are used, which either ignore censoring or CEs. In an empirical study including randomized clinical trials from several sponsor organisations, these potential sources of bias are investigated. The main aim is to compare the estimators that are typically used in AE analysis to the Aalen-Johansen estimator (AJE) as the gold-standard. Here, one-sample findings are reported, while a companion paper considers consequences when comparing treatment groups. Estimators are compared with descriptive statistics, graphical displays and with a random effects meta-analysis. The influence of different factors on the size of the bias is investigated in a meta-regression. Comparisons are conducted at the maximum follow-up time and at earlier evaluation time points. CEs definition does not only include death before AE but also end of follow-up for AEs due to events possibly related to the disease course or the treatment. Ten sponsor organisations provided 17 trials including 186 types of AEs. The one minus KME was on average about 1.2-fold larger than the AJE. Leading forces influencing bias were the amount of censoring and of CEs. As a consequence, the average bias using the incidence proportion was less than 5%. Assuming constant hazards using incidence densities was hardly an issue provided that CEs were accounted for. There is a need to improve the guidelines of reporting risks of AEs so that the KME and the incidence proportion are replaced by the AJE with an appropriate definition of CEs.