ABSTRACT
Randomized controlled trials (RCTs) are essential to support clinical decision making. We assessed the transparency, completeness and consistency of reporting of 244 reports (120 peer-reviewed journal publications; 124 preprints) of RCTs assessing pharmacological interventions for the treatment of COVID-19 published the first 17 months of the pandemic (up to May 31, 2021). Transparency was poor. Only 55% of trials were prospectively registered; 39% made their full protocols available and 29% provided access to their statistical analysis plan. Only 6% completely reported the most important information. Primary outcome(s) reported in trial registries and published reports were inconsistent in 47% of trials. Of the 124 RCTs published as preprint, 76 were secondarily published in a peer-reviewed journal. There was no major improvement after the peer-review process. Lack of transparency, completeness and consistency of reporting is an important barrier to trust, interpretation and synthesis in COVID-19 clinical trials.
ABSTRACT
"Living" evidence synthesis is of primary interest for decision-makers to overcome the COVID-19 pandemic. The COVID-NMA provides open-access living meta-analyses assessing different therapeutic and preventive interventions. Data are posted on a platform (https://covid-nma.com/) and analyses are updated every week. However, guideline developers and other stakeholders also need to investigate the data and perform their own analyses. This requires resources, time, statistical expertise, and software knowledge. To assist them, we created the "metaCOVID" application which, based on automation processes, facilitates the fast exploration of the data and the conduct of analyses tailored to end-users needs. metaCOVID has been created in R and is freely available as an R-Shiny application. The application conducts living meta-analyses for every outcome. Several options are available for subgroup and sensitivity analyses. The results are presented in downloadable forest plots. metaCOVID is freely available from https://covid-nma.com/metacovid/ and the source code from https://github.com/TEvrenoglou/metaCovid.