Quantifying the impact of the COVID-19 pandemic on clinical trial screening rates over time in 37 countries.

The COVID-19 pandemic has had an unprecedented and disruptive impact on people's health and lives worldwide. In addition to burdening people's health in the short-term in the form of infection, illness, and mortality, there has been an enormous negative impact on clinical research. Clinical trials experienced challenges in ensuring patient safety and enrolling new patients throughout the pandemic. Here, we investigate and quantify the negative impact that the COVID-19 pandemic has industry-sponsored clinical trials, both in the USA and worldwide. We find a negative correlation between the severity of the COVID-19 pandemic and clinical trial screening rate, with the relationship being strongest during the first three months of the pandemic compared to the entire duration of the pandemic. This negative statistical relationship holds across therapeutic areas, across states in the USA despite the heterogeneity of responses at the state-level, and across countries. This work has significant implications for the management of clinical trials worldwide in response to the fluctuating severity of COVID-19 moving forward and for future pandemics.

Enhancing pediatric clinical trial feasibility through the use of Bayesian statistics.

BackgroundPediatric clinical trials commonly experience recruitment challenges including limited number of patients and investigators, inclusion/exclusion criteria that further reduce the patient pool, and a competitive research landscape created by pediatric regulatory commitments. To overcome these challenges, innovative approaches are needed.MethodsThis article explores the use of Bayesian statistics to improve pediatric trial feasibility, using pediatric Type-2 diabetes as an example. Data for six therapies approved for adults were used to perform simulations to determine the impact on pediatric trial size.ResultsWhen the number of adult patients contributing to the simulation was assumed to be the same as the number of patients to be enrolled in the pediatric trial, the pediatric trial size was reduced by 75-78% when compared with a frequentist statistical approach, but was associated with a 34-45% false-positive rate. In subsequent simulations, greater control was exerted over the false-positive rate by decreasing the contribution of the adult data. A 30-33% reduction in trial size was achieved when false-positives were held to less than 10%.ConclusionReducing the trial size through the use of Bayesian statistics would facilitate completion of pediatric trials, enabling drugs to be labeled appropriately for children.