Learning from Data on Trials

Understanding the clinical trials system requires looking at detailed data on clinical trials. Today, we provide a snapshot of our results from combining databases from two different sources: the AACT database from ClinicalTrials.gov and the Biomedtracker database from Informa Business Intelligence. Recognizing that different therapeutic areas differ significantly in terms of protocol design, the analysis was developed to be disease-specific.

As a first step, we present here likelihood of trial success and time taken to approval. Note that these results do not necessarily indicate a fair comparison in terms of performance across disease; certain diseases have much more complex trial design and can simply be difficult to design cures for. But this analysis shows right away that a one-size fits all gold standard of conducting clinical trials might not be the best way to go. As the weeks go on, we will show how other factors than simply disease-type can be incredibly important in deciding how long a trial goes and its likelihood of success.

.Erin Todaro, Felipe Feijoo, Jen Bernstein, Sauleh Siddiqui

JHU Research Team