JMIR Publications

ABSTRACT

This article will provide a perspective on data sharing practices in the context of the novel coronavirus disease (COVID-19) pandemic. The scientific community has made several important inroads in the fight against COVID-19 and there are over 2,500 clinical trials registered globally. Within the rapidly changing pandemic, we are seeing a large number of trials conducted without results made available. It is likely that a plethora of trials have stopped early, not for statistical reasons, but due to lack of feasibility. Trials stopped early for feasibility are, by definition, statistically underpowered and thereby prone to inconclusive findings. Statistical power is not necessarily linear with total sample size and even small reductions in patient numbers or events can have a substantial impact. Given the profusion of clinical trials investigating identical or similar treatments across different geographical and clinical contexts, one must also consider that the likelihood of a Type 1 error can become inflated with the increasing number of trials. Complicating this is the evolving nature of the pandemic, where baseline assumptions on control group risk factors used to develop sample size calculations are far more challenging than in well-documented diseases. The standard answer to these challenges during non-pandemic settings is to assess each trial for statistical power and risk-of-bias and then pool the reported aggregated results using meta-analytic approaches. This solution simply will not suffice for COVID-19. Even with random-effects meta-analysis models, it will be difficult to adjust for heterogeneity of different trials with aggregated reported data alone, especially given the absence of common data standards and outcome measures. To date, several groups have proposed structures and partnerships for data sharing. As COVID-19 has forced reconsideration of policies, processes, and interests, this is the time to advance scientific cooperation and shift the clinical research enterprise toward a data-sharing culture to maximize our response in the service of public health.