ArticlePloS one2016
Using Clinical Trial Simulators to Analyse the Sources of Variance in Clinical Trials of Novel Therapies for Acute Viral Infections.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- A multiplex protein panel assay for severity prediction and outcome prognosis in patients with COVID-19: An observational multi-cohort study.EClinicalMedicine · 2022Article
- Antiviral effect of high-dose ivermectin in adults with COVID-19: A proof-of-concept randomized trial.EClinicalMedicine · 2021Article
- Application of the Nested Enzyme-Within-Enterocyte (NEWE) Turnover Model for Predicting the Time Course of Pharmacodynamic Effects.CPT: pharmacometrics & systems pharmacology · 2020Article
- Designing antifilarial drug trials using clinical trial simulators.Nature communications · 2020Article
- Existence of a Conserved Quantity and Stability ofComputational and mathematical methods in medicine · 2019Article
- Pharmacokinetic interactions and dosing rationale for antiepileptic drugs in adults and children.British journal of clinical pharmacology · 2018Article
- Assessing the interruption of the transmission of human helminths with mass drug administration alone: optimizing the design of cluster randomized trials.Parasites & vectors · 2017Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAbout 90% of drugs fail in clinical development. The question is whether trials fail because of insufficient efficacy of the new treatment, or rather because of poor trial design that is unable to detect the true efficacy. The variance of the measured endpoints is a major, largely underestimated source of uncertainty in clinical trial design, particularly in acute viral infections. We use a clinical trial simulator to demonstrate how a thorough consideration of the variability inherent in clinical trials of novel therapies for acute viral infections can improve trial design. METHODS AND
findingsWe developed a clinical trial simulator to analyse the impact of three different types of variation on the outcome of a challenge study of influenza treatments for infected patients, including individual patient variability in the response to the drug, the variance of the measurement procedure, and the variance of the lower limit of quantification of endpoint measurements. In addition, we investigated the impact of protocol variation on clinical trial outcome. We found that the greatest source of variance was inter-individual variability in the natural course of infection. Running a larger phase II study can save up to $38 million, if an unlikely to succeed phase III trial is avoided. In addition, low-sensitivity viral load assays can lead to falsely negative trial outcomes.
conclusionsDue to high inter-individual variability in natural infection, the most important variable in clinical trial design for challenge studies of potential novel influenza treatments is the number of participants. 100 participants are preferable over 50. Using more sensitive viral load assays increases the probability of a positive trial outcome, but may in some circumstances lead to false positive outcomes. Clinical trial simulations are powerful tools to identify the most important sources of variance in clinical trials and thereby help improve trial design.
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