Despite continuous development of, and the quarterly updates to, CDISC Controlled Terminology, there is still a need for study-specific extensions to standard codelists.
Often, extensions to standard terminology are incorrect. For example, some users falsely believe that any new terms can be added to an extensible codelist and related data validation is not needed if new terms are listed in the define.xml. In this webinar, Sergiy Sirichenko will summarize industry metrics across many studies and sponsors to produce an overall picture of CDISC Controlled Terminology utilization. He will review common issues with implementation of controlled terminology and reinforce best practices for extending standard codelists.
SDTM Trial Summary (TS) domain is essential for regulatory submission and is part of FDA rejection criteria.
TS can be complex to implement with guidance spread across SDTMIG, FDA Study Data Technical Conformance Guide (TCG), FDA Technical Rejection Criteria, and numerous terminologies. Interpretation of validation results for TS can also be difficult. In this webinar, Kristin Kelly discusses the minimum set of parameters that should be included in TS and reviews common implementation issues.
The COVID-19 pandemic had an immense impact on people’s lives. The quick development of effective and safe vaccines as well as their availability are critical to public health.
Clinical trials for vaccines typically collect similar types of data. Preparing this data for submission requires knowledge of specific guidance from regulatory agencies and standards development organizations. In this webinar, Michael Beers reviews FDA, PMDA, and CDISC guidance and other considerations for preparing vaccine studies for regulatory submissions. Also, Michael goes over common problems and inconsistencies seen in data of vaccine studies.
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.
Not Submitted Annotations
For any information that is on the CRF but not mapped to SDTM, annotate the page and/or field with "NOT SUBMITTED."
(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.
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