In today’s world, clinical trials are expensive, complex, multi-disciplinary processes involving multiple partner entities working together to satisfy stringent regulations to improve patient lives by bringing innovative therapy to the market. Due to the competitive nature of our industry, there is a push towards increasing efficiency in order to deliver an on-time, on-budget accurate representation of clinical trial data to regulatory authorities.
“Just fix it, we have too much to do.”
This is a common phrase often heard in organizations and especially in SAS® programming departments. It is relatively easy to fix programming issues each time they occur rather than look into why it occurred in the first place. The problem with this approach is that there is a good chance that a similar issue may occur and not be caught next time resulting in the compromised validity of deliverables. This is analogous to treating the symptom every time it occurs and not looking deeper into the actual cause.
In order to prevent systemic programming issues from occurring, it is essential to identify what, how and why an undesirable event occurred before taking steps to prevent it in the future. A systematic process of investigating and identifying root cause that can be controlled by the programming/management team with an intention of preventing it in the future can be done through Root Cause Analysis (RCA).
This paper discusses how we utilized principles of RCA in statistical programming and reporting to identify root cause(s) and came up with a corrective and preventive action plan (CAPA) after an undesirable event in the form of an adverse event report with incorrect numbers was produced, passed validation and multiple reviews by the study team, and was submitted to the regulatory authority before the issue was found.