Are your older study data ready for submission?
Why data from older studies must be prepared to align with current requirements
Preparing data from an older study for regulatory submission is no small feat.
Standards and requirements for clinical data submission have evolved significantly over the years, making alignment with the latest CDISC standards both challenging and essential.
Submitting data that does not meet these standards can delay approvals, increase costs, and even risk the study’s success.
In this blog post, we will walk through the key steps for ensuring that your older study data are prepared correctly for submission, including essential documentation and best practices.
Essential documents for preparing older study data
When converting data from an older study to meet submission standards, having the right documentation is critical to provide essential context, support accurate mapping, compliance, and an efficient review process by regulatory authorities.
Below are the following key documents to have prepared for submission:
Please note: It is important to mention that these suggestions and recommendations are mostly seen from a programmers perspective, whereas there are potentially many other documents required by authorities.
As the foundation of your study data, the protocol used in the study is indispensable.
This document details essential study information, including its design, the timing of visits and tests, and the in/exclusion criteria applied throughout the study, including any updates.
This overview ensures that programmers and data managers have the necessary context to accurately convert data into standardized formats like SDTM.
A version of the CRF (especially an annotated version, if available) is also invaluable for understanding how study data were collected.
The CRF clarifies where each piece of data originated from and how variables were defined, making it key to understanding the structure of collected data and facilitating the mapping process to CDISC standards.
Having access to all original data, including external sources such as laboratory results or ECG data, is important for accuracy.
Data that has not been captured within the CRF may need special handling to align with CDISC standards, as these external datasets often contain unique variables or structures.
For submission-ready data, especially in ADaM format, the SAP is critical.
This document outlines the statistical methodologies used in the original study, helping to ensure that derived variables and analytic approaches are consistent with the original results.
The TFLs represent the study’s final reported outcomes, serving as a reference to validate that the converted data produces consistent results.
Having access to these outputs ensures that no key findings are compromised during the reformatting process.
Finally, derived datasets and their documentation provide a reliable reference for complex calculations or transformations.
These documents help ensure consistency in data derivations, particularly for transformations or derived variables that may require special attention during the conversion process.
How a data conversion can be approached
Achieving submission-ready formats like SDTM and ADaM requires more than gathering the right documentation.
This is not to neglect the importance of documentation – only to highlight the importance of the approach to ensure an ideal conversion.
Here are some of our best practices during the conversion process that can streamline the effort and improve compliance.
Original program documentation
Where it is possible, the access to the original programs used to create derived datasets and outputs, is appreciated, but not necessary.
If discrepancies arise between the converted data and the original findings, these programs allow teams to trace and resolve any issues efficiently, ensuring transparency and alignment with the study’s original outcomes.
Document discrepancies and decisions thoroughly
Older data may not always fit neatly into today’s standards.
Documenting each adjustment – whether it is a missing value, non-standard variable, or a known discrepancy – are essential.
Detailed documentation allows regulatory reviewers to understand how challenges were addressed, supporting a more efficient review process.
Are you unsure about submitting your older study data?
We can advise you on managing older study data and help map your data according to CDISC standards.
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