Clinical Development

Presentation

Food Allergy Research with CDISC Standards

In February 2024, the FDA approved a treatment for the reduction of allergic reactions to multiple foods based on results from the NIH-sponsored OUtMATCH study. Many federal studies like this one do not require CDISC standards, however in this case SDTM and ADaM were required because OUtMATCH used a commercial product and intended to submit the results for agency approval.

This presentation discusses the ways in which the SDTM tabulation process improved the overall data quality for the study, summarizes a strategy for mapping all the data collected during a food challenge data into standard SDTM domains, and explains the ADaM structures used to capture endpoints. Overall, it provides an example of a study in which the CDISC standards played a key role throughout the process and provides more specific details on applying the standards to research on food allergy treatments.

Presentation

Navigating Complexities: Integrating Data from Ongoing Studies in Regulatory Submissions

Integrating data for regulatory submissions requires organizing and consolidating information from multiple studies for a project. The activities could include aligning variables from studies using different versions of CDISC standards and/or creating new variables to support integrated analyses. BUT, what happens when another layer of complexity is added? What if an ISS or ISE will include data from ongoing studies? What are key considerations when dealing with ongoing data?

Blog

FMQs vs SMQs 

Sponsors often rely on Standardized MedDRA Queries (SMQs) to group adverse events for detection of safety signals across clinical trials. Additionally, the FDA has introduced their own version of adverse event groupings – FDA MedDRA Queries (FMQs). So, what’s the difference? 

Blog

Statistical Challenges with Site Enrollment in Clinical Trials 

Did you know that insufficient enrollment is the leading cause for clinical trials being halted? Study sponsors rightly embrace those sites which are high performing as they give a study the best opportunity to meet its enrollment targets. However, is it possible for there to be overreliance on these high enrolling sites? Unfortunately, the answer is yes. 

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