c212: An R Package for the Detection of Safety Signals in Clinical Trials Using Body-Systems (System Organ Classes)

Carragher, Raymond; Robertson, Chris

doi:10.21105/joss.02706

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Safety analysis in clinical trials is a complex problem which continues to be an area of interest to researchers with new methods continuing to be developed 23–25 . The methods compared here are primarily suited to an end of trail incidence analysis, and the availability of software implementations 11,12 has the potential to allow the integration of the methods into a trial's safety analysis with relatively small overhead, and is something that should be considered for inclusion 26…”

Section: Discussionmentioning

confidence: 99%

“…By using relationships between outcomes grouped methods offer the possibility of detecting more AEs in the pre‐marketing phase of a treatment lifecycle, and with their routine use becoming more common, 7–10 aided by the existence of implementations of a number of the methods, 11,12 an understanding of their suitability is an important factor when analysing the methods' results. Characteristics which may influence the choice of method are not only the power and error rates, but also the effect the data structure has on the methods' performances.…”

Section: Introductionmentioning

confidence: 99%

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

Carragher

Robertson

2021

Pharmaceutical Statistics

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Carragher

Robertson

2021

Pharmaceutical Statistics

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Statistical methods leveraging the hierarchical structure of adverse events for signal detection in clinical trials: a scoping review of the methodological literature

de Abreu Nunes,

Hooper,

McGettigan

et al. 2024

BMC Med Res Methodol

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Background In randomised controlled trials with efficacy-related primary outcomes, adverse events are collected to monitor potential intervention harms. The analysis of adverse event data is challenging, due to the complex nature of the data and the large number of unprespecified outcomes. This is compounded by a lack of guidance on best analysis approaches, resulting in widespread inadequate practices and the use of overly simplistic methods; leading to sub-optimal exploitation of these rich datasets. To address the complexities of adverse events analysis, statistical methods are proposed that leverage existing structures within the data, for instance by considering groupings of adverse events based on biological or clinical relationships. Methods We conducted a methodological scoping review of the literature to identify all existing methods using structures within the data to detect signals for adverse reactions in a trial. Embase, MEDLINE, Scopus and Web of Science databases were systematically searched. We reviewed the analysis approaches of each method, extracted methodological characteristics and constructed a narrative summary of the findings. Results We identified 18 different methods from 14 sources. These were categorised as either Bayesian approaches (n=11), which flagged events based on posterior estimates of treatment effects, or error controlling procedures (n=7), which flagged events based on adjusted p-values while controlling for some type of error rate. We identified 5 defining methodological characteristics: the type of outcomes considered (e.g. binary outcomes), the nature of the data (e.g. summary data), the timing of the analysis (e.g. final analysis), the restrictions on the events considered (e.g. rare events) and the grouping systems used. Conclusions We found a large number of analysis methods that use the group structures of adverse events. Continuous methodological developments in this area highlight the growing awareness that better practices are needed. The use of more adequate analysis methods could help trialists obtain a better picture of the safety-risk profile of an intervention. The results of this review can be used by statisticians to better understand the current methodological landscape and identify suitable methods for data analysis - although further research is needed to determine which methods are best suited and create adequate recommendations.

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