Controlled Terminology have you confused? We got you.
When starting a new clinical study and preparing the data collection design, you may ask yourself: How important is Controlled Terminology (CT)? How do I prepare the data collection process to be CDISC compliant and avoid rework for submissions? We often receive questions such as these. Whether you are newer to clinical studies or a seasoned veteran, CT can often be an elusive and confusing topic. Having a better understanding of CT standards and requirements, as well as CT-related issues found in validation checks, can improve your submission data and processes.
P21 Validation Engine Improvements
The P21 Validation Engines are consistently updated and improved upon with insights from our Subject Matter Experts, consultations with regulatory agencies, and findings submitted by our users. Examples include:
AD1012 has been split into two rules: AD1012 and AD1012A. The former checks for custom variables and is a Warning; the later checks for standard variables and is an Error. These rules consider secondary variable names ending in *N or *C, for numeric or character equivalent, respectively.
AD0047 was producing problems for some variables but has already been fixed and patched for over a year.
(Originally published on October 6, 2020. Last Updated on October 30, 2020)
Effective October 1st, 2020, China’s NMPA will accept CDISC submissions. To support this initiative, Pinnacle 21 has released a new Chinese-language validation engine, available now in both Enterprise and Community. This engine supports datasets with Chinese-encoded characters and displays rule messages and descriptions in Chinese translation.
Versions and Revisions
You need to annotate and submit only the unique forms from the final version of the CRF, provided that they cover all the collected data. Combine all unique pages, e.g., those for clinical data and central review data, into a single acrf.pdf. Here are some example scenarios:
ADaM data are required by the FDA and PMDA, and accepted by China’s NMPA. Agencies often begin reviews with ADaM data validation, which helps them understand the analyses performed and reproduce results.
In the regulatory review process, it's critical to have analysis data that comply with the CDISC ADaM standard. Both the FDA and PMDA require ADaM data, and as they begin reviews, they start with ADaM data validation. ADaM data help these agencies understand the analyses performed and reproduce the results for further validation.
In this webinar, Trevor Mankus covers the more commonly occurring validation rules and some potential reasons why they fired.
The SDTM annotated CRF (aCRF) is a cumbersome submission document to create. It's also highly important. It visually documents how data are mapped from the CRF to SDTM. Because this is mostly a manual task, it is key to know what makes a high-quality aCRF.
In this webinar, Amy Garrett reviews published guidance from regulatory agencies and provides best practices for CRF annotations. These practices ensure your aCRF meets current regulatory requirements and the needs of internal users.
SUPPQUAL datasets represent the non-standard variables in SDTM tabulation data. However, there is a lack of implementation metrics across the industry to understand the actual usage of SUPPQUAL datasets. In this webinar, Sergiy Sirichenko summarizes metrics from many studies and sponsors to produce an overall picture.
When preparing data for regulatory submissions, we know you need to comply with hundreds of validation rules. While many rules are straightforward, some could be confusing. Are you wondering why a certain validation rule fired? If it’s applicable to your study? And whether you should fix it or explain it? These and other commonly asked questions were answered by Pinnacle 21’s Michael Beers in a recently hosted webinar. You can watch the recording below. For webinar slides and frequently asked questions, read on.
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