By Alexandre Mouravskiy
If you had to pick one buzzword that perfectly encapsulated the current latest and greatest in innovation, you could do a lot worse than “big data”. The last few years have seen big data and data mining introduced into nearly every field imaginable, often with disruptive results. Now, that same set of tools is being leveraged to improve drug development, with huge potential implications in identifying and correcting issues that arise during clinical trials. While there are no set standards or best practices around marshalling big data to identify and correct failing trials, we wanted to throw out a few ideas and suggestions that could go a long way towards making clinical trial succeed.
We see big data making the biggest impact in the areas of patient recruitment, process monitoring, and safety and data handling. In this post, we will take a quick overview of what big data is, how we look at and define program failures and faltering trials, and do a quick fly-by of the topics we will be covering in the future. Over future blog posts, we will be exploring each of these in depth to identify where and how the promises of big data can best increase the efficiency and success of your clinical trials.