Projects per year
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.
- adverse events
- system organ class
- Bayesian hierarchy
- false discovery rate
FingerprintDive into the research topics of 'Assessing safety at the end of clinical trials using system organ classes: a case and comparative study'. Together they form a unique fingerprint.
- 2 Finished
14/02/18 → 21/08/21
Project: Research Fellowship
1/02/13 → 19/10/17
Project: Research Studentship - Internally Allocated
Comparative study for: "Assessing safety at the end of clinical trials using system organ classes: a case and comparative study"
Carragher, R. B. (Creator), University of Strathclyde, 15 Jun 2021
Methods for detecting safety signals in clinical trials using groupings of adverse events by body-system or system organ class.
Carragher, R. B. (Creator), Zenodo, 30 May 2019
- 2 Citations
- 1 Article
c212 : An R package for the detection of safety signals in clinical trials using body-systems (System Organ Classes)Carragher, R. & Robertson, C., 4 Dec 2020, In: Journal of Open Source Software. 5, 56, 6 p., 2706.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile35 Downloads (Pure)