Sponsors are generally aware of the commonly held Type B FDA meetings, from pre-IND to End of Phase 2 (EOP2) to pre-NDA/BLA, but how often do you take advantage of additional Type C meetings for agency feedback? Continued discussion and input from the agency can be very beneficial, even outside of the milestones that allow for a Type B Meeting.
According to the Study Data Technical Conformance Guide (October 2024), sponsors should prepare a document called the study data standardization plan (SDSP) for each of their development programs to describe the submission of standardized non-clinical and clinical study data to the FDA.
Most of those who work with Clinical Data Interchange Standards Consortium (CDISC) datasets have probably heard of the term “traceability.” But why is it important and how can it best be implemented?
What’s the secret to optimizing your clinical trial? It could be understanding enrollment caps and knowing exactly when to set them.
Do I need to up-version to the most recent versions of the standard from the current Catalog when preparing to submit clinical study data? How can I figure this out? Check out the latest blog from Rho to find out.
In clinical trials, the accuracy and integrity of data are paramount. While the goal is to handle data systematically and programmatically, there are occasions when hardcoding becomes necessary. Note the following considerations for when to hardcode and the importance of documenting these decisions.
Interim sample size adjustments and their many approaches are a frequent discussion point between Sponsors and statisticians during protocol development. One such approach is a blinded assessment of variance, favored by some Sponsors for its lack of alpha penalty. We will discuss how this method works, the pros, the cons, and if this approach might be appropriate for your protocol.
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.
In the fast-paced world of clinical research, efficiency is a key priority. Splitting SDTM domains into multiple datasets can serve as a valuable tool, streamlining the process of creating and reviewing large, complex data domains.
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?
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