4 research outputs found
Survival analysis for AdVerse events with VarYing follow-up times (SAVVY): Rationale and statistical concept of a meta-analytic study
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
Survival analysis for AdVerse events with VarYing follow-up times (SAVVY) -- comparison of adverse event risks in randomized controlled trials
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
Survival analysis for AdVerse events with VarYing follow-up times (SAVVY) -- estimation of adverse event risks
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