Register Now: Disruptive Innovations in Data Analytics in CNS Clinical Trials

Register now for Bracket’s upcoming webinar on utilizing data analytics in CNS clinical trials, presented by:

admin-ajax Alan Kott, MUDr, Clinical Vice President and Practice Lead, Data Analytics, Bracket

daniel-david David G. Daniel, MD, Senior Vice President and Chief Medical Officer, Bracket, and Professor of Psychiatry, George Washington University

Measurement error and data recording errors (including data fabrication) (Baigent, 2008) are frequent and often non-randomly distributed in clinical trial datasets. Even when accumulated at a single site or a small number of sites their impact may be devastating to the success of a CNS clinical trial. It is therefore critical to identify those sites and raters who are at greatest risk of providing erroneous or otherwise compromised data.

Fig1_Any Data Quality ConcernCentral statistical monitoring techniques have been shown to successfully identify fraudulent/fabricated data in clinical trials using objective outcomes (Knepper, 2016) but have been less effective in clinical trials using subjective outcome measures such as psychiatric rating scales (O’Kelly, 2004). It is however exactly these ‘subjective outcome’ trials that are in the greatest need of effective data quality monitoring programs.

At Bracket, we have successfully implemented risk-based data quality monitoring programs (Blinded Data Analytics) in a number of CNS clinical trials across numerous indications. These programs combined near real time central statistical monitoring approaches with comprehensive clinical review and where indicated targeted follow-up with the identified raters/sites. In many of these CNS clinical trials, central statistical monitoring worked in concert with intelligent eCOA and remote audio/video monitoring programs.

In this webinar, we will discuss examples of identified data quality concerns coming from CNS clinical trials and the impact of their presence on study outcomes. We will show that many of the data quality concerns are clustering at a small number of centers and many post-randomization data quality concerns can be predicted by the occurrence of similar concerns in the screening period. We will conclude by demonstrating the positive impact of implementing eCOA and remote audio/video monitoring programs on data quality.

To reserve your spot, register now.