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.
In August 2020, we released our new Pinnacle 21 validation engine “FDA (1907.2)” for preparing study data for FDA submissions. It includes the validation rules currently used by FDA’s DataFit, the agency’s implementation of Pinnacle 21 Enterprise.
The previous, outdated version of the validation rules is represented by our “FDA Legacy (1903.1)” engine. It will be available for the next few months until our next release. This window allows you to finalize your ongoing submission preparations.
Tables of Contents and Bookmarks
A table of contents with hyperlinks to bookmarked pages is seen in CDISC's sample aCRF (avaliable in the Define-XML v2.0 download). Both the Metadata Submission Guidelines (MSG) from CDISC and the PDF specifications from the FDA require aCRFs to have hyperlinks to bookmarked pages. The PDF specifications also require a table of contents.
Intro to aCRF Formatting
"How should I best format the annotations on my CRFs?" We get that a lot. The requirements may feel murky. Users often mimic CDISC’s sample aCRF (in the Define-XML v2.0 download). But even then, you might lack confidence on the specific formatting details, and wonder how to fit your content into the usable space.
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.
Our recent webinar Confusing Validation Rules Explained generated lots of follow-up questions from you. We are addressing those questions in a series of posts. In this edition, we will clarify the meaning and purpose of the duplicate records validation rules, then answer some frequently asked questions about duplicate records.
Our recent webinar Confusing Validation Rules Explained sparked lots of follow-up questions from you. We are addressing those questions in a series of posts. In this edition, we will clarify the best practices when mapping data for screen failure, not assigned, and not treated subjects. We will also help by describing the most effective ways to respond to these situations.
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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