Assessing safety at the end of clinical trials using system organ classes: A case and comparative study

Recent approaches to the statistical analysis of adverse event (AE) data in clinical trials have proposed the use of groupings of related AEs, such as by system organ class (SOC). These methods have opened up the possibility of scanning large numbers of AEs while controlling for multiple comparisons...

Full description

Bibliographic Details
Main Authors: Carragher, R. (Author), Robertson, C. (Author)
Format: Article
Language:English
Published: John Wiley and Sons Ltd 2021
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02680nam a2200373Ia 4500
001 10.1002-pst.2148
008 220427s2021 CNT 000 0 und d
020 |a 15391604 (ISSN) 
245 1 0 |a Assessing safety at the end of clinical trials using system organ classes: A case and comparative study 
260 0 |b John Wiley and Sons Ltd  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1002/pst.2148 
520 3 |a Recent approaches to the statistical analysis of adverse event (AE) data in clinical trials have proposed the use of groupings of related AEs, such as by system organ class (SOC). These methods have opened up the possibility of scanning large numbers of AEs while controlling for multiple comparisons, making the comparative performance of the different methods in terms of AE detection and error rates of interest to investigators. We apply two Bayesian models and two procedures for controlling the false discovery rate (FDR), which use groupings of AEs, to real clinical trial safety data. We find that while the Bayesian models are appropriate for the full data set, the error controlling methods only give similar results to the Bayesian methods when low incidence AEs are removed. A simulation study is used to compare the relative performances of the methods. We investigate the differences between the methods over full trial data sets, and over data sets with low incidence AEs and SOCs removed. We find that while the removal of low incidence AEs increases the power of the error controlling procedures, the estimated power of the Bayesian methods remains relatively constant over all data sizes. Automatic removal of low-incidence AEs however does have an effect on the error rates of all the methods, and a clinically guided approach to their removal is needed. Overall we found that the Bayesian approaches are particularly useful for scanning the large amounts of AE data gathered. © 2021 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd. 
650 0 4 |a adverse events 
650 0 4 |a article 
650 0 4 |a Bayes theorem 
650 0 4 |a Bayes Theorem 
650 0 4 |a Bayesian hierarchy 
650 0 4 |a computer simulation 
650 0 4 |a Computer Simulation 
650 0 4 |a controlled study 
650 0 4 |a false discovery rate 
650 0 4 |a false discovery rate 
650 0 4 |a human 
650 0 4 |a Humans 
650 0 4 |a incidence 
650 0 4 |a intermethod comparison 
650 0 4 |a methodology 
650 0 4 |a Research Design 
650 0 4 |a safety 
650 0 4 |a simulation 
650 0 4 |a system organ class 
700 1 |a Carragher, R.  |e author 
700 1 |a Robertson, C.  |e author 
773 |t Pharmaceutical Statistics