Consequences of Improper Data Collection

In a recent video interview with Applied Clinical Trials, Erin Erginer, director of product, Certara discussed the growing complexity and volume of data in clinical trials. Data is now captured from biospecimens and digital health technologies, including advanced genetic and imaging data. Regulatory agencies require standardized data submission for analysis, but do not dictate collection methods, leaving pharma companies to develop their own standards. Lack of standards leads to inefficiencies, with only 20% of studies meeting deadlines, causing significant delays and costs. Improper data collection can lead to missed endpoints and regulatory issues. Collaboration among pharma companies and external vendors is crucial for improving data standards and trial efficiency.

A transcript of Erginer’s conversation with ACT can be found below.

ACT: What are some consequences of data not being collected properly?

Erginer: I think there are a lot of pretty strong indicators that the industry is struggling right now to deal with all of this data. Only one in five studies are able to meet their original planned deadlines. Those deadlines are typically an average of over a year beyond what their predicted time was going to be, so that's huge cost to the industry, and obviously delays in getting patients the drugs that they need. There's also an increase of end-of-study timelines in the last five years of 32%. Again, each of those days of delays cost an average of $600,000 to $8 million a day, so that's having a big consequence. Those consequences are pretty clear when you look at that information. In addition to the impact on timelines and cost, one of the most significant ones I've seen happen several times in the past is that if you don't have a good understanding of all the data that's being collected at the very beginning and ensure that you're collecting everything that's required to be reported at the end, you can miss information. If a scientist, if the biostatistician doesn't have a piece of the formula he needs, and able to do whatever the algorithms are and actually make calculations, you can't go back and collect that data again. It doesn't exist, so you just aren't able to report those endpoints that are very critical to the trial. The consequences for timelines as well as reportability of endpoints are pretty serious.