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

Carragher, Raymond; Robertson, Chris

doi:10.1002/pst.2148

Cited by 5 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AE and SAEs were grouped by the organ class system ( 22 ). There were no prespecified futility margins for the DSMB; however, the DSMB was entitled to recommend termination of the study.…”

Section: Methodsmentioning

confidence: 99%

Recombinant C1 inhibitor in the prevention of severe COVID-19: a randomized, open-label, multi-center phase IIa trial

Urwyler,

Leimbacher,

Charitos

et al. 2023

Front. Immunol.

View full text Add to dashboard Cite

BackgroundConestat alfa (ConA), a recombinant human C1 inhibitor, may prevent thromboinflammation.MethodsWe conducted a randomized, open-label, multi-national clinical trial in which hospitalized adults at risk for progression to severe COVID-19 were assigned in a 2:1 ratio to receive either 3 days of ConA plus standard of care (SOC) or SOC alone. Primary and secondary endpoints were day 7 disease severity on the WHO Ordinal Scale, time to clinical improvement within 14 days, and safety, respectively.ResultsThe trial was prematurely terminated because of futility after randomization of 84 patients, 56 in the ConA and 28 in the control arm. At baseline, higher WHO Ordinal Scale scores were more frequently observed in the ConA than in the control arm. On day 7, no relevant differences in the primary outcome were noted between the two arms (p = 0.11). The median time to defervescence was 3 days, and the median time to clinical improvement was 7 days in both arms (p = 0.22 and 0.56, respectively). Activation of plasma cascades and endothelial cells over time was similar in both groups. The incidence of adverse events (AEs) was higher in the intervention arm (any AE, 30% with ConA vs. 19% with SOC alone; serious AE, 27% vs. 15%; death, 11% vs. 0%). None of these were judged as being related to the study drug.ConclusionThe study results do not support the use of ConA to prevent COVID-19 progression.Clinical trial registrationhttps://clinicaltrials.gov, identifier NCT04414631.

show abstract

“…AE and SAEs were grouped by the organ class system ( 22 ). There were no prespecified futility margins for the DSMB; however, the DSMB was entitled to recommend termination of the study.…”

Section: Methodsmentioning

confidence: 99%

Recombinant C1 inhibitor in the prevention of severe COVID-19: a randomized, open-label, multi-center phase IIa trial

Urwyler,

Leimbacher,

Charitos

et al. 2023

Front. Immunol.

View full text Add to dashboard Cite

show abstract

“…Bayesian modelling is used in a variety of different areas related to clinical trials such as adaptive designs, 10 meta-analyses 11 and assessing safety, 12 while we present a Bayesian framework that allows the probability distributions of the effect sizes of a group of related combination therapies to be updated based on the outcome of a single combination study. This allows the PoS of related combination studies to be updated.…”

Section: Introductionmentioning

confidence: 99%

Updating the probability of study success for combination therapies using related combination study data

Graham

Harbron

Jaki

2023

Stat Methods Med Res

View full text Add to dashboard Cite

Combination therapies are becoming increasingly used in a range of therapeutic areas such as oncology and infectious diseases, providing potential benefits such as minimising drug resistance and toxicity. Sets of combination studies may be related, for example, if they have at least one treatment in common and are used in the same indication. In this setting, value can be gained by sharing information between related combination studies. We present a framework that allows the study success probabilities of a set of related combination therapies to be updated based on the outcome of a single combination study. This allows us to incorporate both direct and indirect data on a combination therapy in the decision-making process for future studies. We also provide a robustification that accounts for the fact that the prior assumptions on the correlation structure of the set of combination therapies may be incorrect. We show how this framework can be used in practice and highlight the use of the study success probabilities in the planning of clinical studies.

show abstract

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

Carragher

Robertson

2021

Pharmaceutical Statistics

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 5 publications

References 20 publications

Recombinant C1 inhibitor in the prevention of severe COVID-19: a randomized, open-label, multi-center phase IIa trial

Recombinant C1 inhibitor in the prevention of severe COVID-19: a randomized, open-label, multi-center phase IIa trial

Updating the probability of study success for combination therapies using related combination study data

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

Contact Info

Product

Resources

About