On estimands and the analysis of adverse events in the presence of varying follow-up times within the benefit assessment of therapies

Steffen Unkel, Marjan Amiri, Norbert Benda, Jan Beyersmann, Dietrich Knoerzer, Katrin Kupas, Frank Langer, Friedhelm Leverkus, Anja Loos, Claudia Ose, Tanja Proctor, Claudia Schmoor, Carsten Schwenke, Guido Skipka, Kristina Unnebrink, Florian Voss, Tim Friede, Steffen Unkel, Marjan Amiri, Norbert Benda, Jan Beyersmann, Dietrich Knoerzer, Katrin Kupas, Frank Langer, Friedhelm Leverkus, Anja Loos, Claudia Ose, Tanja Proctor, Claudia Schmoor, Carsten Schwenke, Guido Skipka, Kristina Unnebrink, Florian Voss, Tim Friede

Abstract

The analysis of adverse events (AEs) is a key component in the assessment of a drug's safety profile. Inappropriate analysis methods may result in misleading conclusions about a therapy's safety and consequently its benefit-risk ratio. The statistical analysis of AEs is complicated by the fact that the follow-up times can vary between the patients included in a clinical trial. This paper takes as its focus the analysis of AE data in the presence of varying follow-up times within the benefit assessment of therapeutic interventions. Instead of approaching this issue directly and solely from an analysis point of view, we first discuss what should be estimated in the context of safety data, leading to the concept of estimands. Although the current discussion on estimands is mainly related to efficacy evaluation, the concept is applicable to safety endpoints as well. Within the framework of estimands, we present statistical methods for analysing AEs with the focus being on the time to the occurrence of the first AE of a specific type. We give recommendations which estimators should be used for the estimands described. Furthermore, we state practical implications of the analysis of AEs in clinical trials and give an overview of examples across different indications. We also provide a review of current practices of health technology assessment (HTA) agencies with respect to the evaluation of safety data. Finally, we describe problems with meta-analyses of AE data and sketch possible solutions.

Keywords: adverse events; benefit assessment; clinical trials; estimands; safety data.

© 2018 The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd.

Figures

Figure 1
Figure 1
Description of different scenarios for typical adverse event (AE) follow‐up (FU) in clinical trials (EoT, end of treatment; Saf‐FU, safety follow‐up; TEAEs, treatment emergent AEs [marked by bold symbols]; V0, visit at the beginning of the trial; V1,…,Vn, visits during treatment). First occurrences of AEs are marked by triangles
Figure 2
Figure 2
Flow chart displaying four different scenarios across indications for the consideration of safety estimands in an HTA system
Figure 3
Figure 3
Cumulative adverse event (AE) probabilities for two groups and constant hazards. Although in group 1 the AE hazard is lower compared to group 0, the cumulative AE probability in group 1 is eventually greater than in group 0
Figure 4
Figure 4
Illustrating example for meta‐analyses. Forest plot of hazard ratios for low trauma fractures as observed in CANVAS and CANVAS‐R with 95% confidence intervals (CIs) and four combined hazard ratios from a fixed‐effect meta‐analysis, modified Knapp‐Hartung (mKH) meta‐analysis, and Bayesian random‐effects meta‐analysis with two half‐normal (HN) priors for the heterogeneity parameter τ

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