COVID-19 interventional trials: Analysis of data sharing intentions during a time of pandemic.

Background: This survey of COVID-19 interventional studies encompasses, and expands upon, a previous publication [1] examining individual participant level data (IPD) sharing intentions for COVID-related trials and publications prior to June 30, 2020.

Methods: Replicating our inclusion criteria from the original survey, we evaluated a larger dataset of 2759 trials and 281 publications in this follow-up survey for willingness to share IPD and studied if sharing sentiment has evolved since the beginning of the pandemic.

Results: We found that 18 months into the pandemic, data sharing intentions remained static at 15% for trials registered through ClinicalTrials.

COVID-19 trials: declarations of data sharing intentions at trial registration and at publication.

Background: The sharing of individual participant-level data from COVID-19 trials would allow re-use and secondary analysis that can help accelerate the identification of effective treatments. The sharing of trial data is not the norm, but the unprecedented pandemic caused by SARS-CoV-2 may serve as an impetus for greater data sharing. We sought to assess the data sharing intentions of interventional COVID-19 trials as declared in trial registrations and publications.

Timely access to trial data in the context of a pandemic: the time is now.

Objective: Clinical trial data sharing has the potential to accelerate scientific progress, answer new lines of scientific inquiry, support reproducibility and prevent redundancy. Vivli, a non-profit organisation, operates a global platform for sharing of individual participant-level trial data and associated documents. Sharing of these data collected from each trial participant enables combining of these data to drive new scientific insights or assess reproducibility-not possible with the aggregate or summary data tables historically made available.

Perspectives on clinical trial data transparency and disclosure.

The increased demand for transparency and disclosure of data from clinical trials sponsored by pharmaceutical companies poses considerable challenges and opportunities from a statistical perspective. A central issue is the need to protect patient privacy and adhere to Good Clinical and Statistical Practices, while ensuring access to patient-level data from clinical trials to the wider research community. This paper offers options to navigate this dilemma and balance competing priorities, with emphasis on the role of good clinical and statistical practices as proven safeguards for scientific integrity, the importance of adopting best practices for reporting of data from secondary analyses, and the need for optimal collaboration among stakeholders to facilitate data sharing.

A data-driven approach to quality risk management.

Aim: An effective clinical trial strategy to ensure patient safety as well as trial quality and efficiency involves an integrated approach, including prospective identification of risk factors, mitigation of the risks through proper study design and execution, and assessment of quality metrics in real-time. Such an integrated quality management plan may also be enhanced by using data-driven techniques to identify risk factors that are most relevant in predicting quality issues associated with a trial. In this paper, we illustrate such an approach using data collected from actual clinical trials.