Leveraging Metadata for SDTM Automation and Compliance


Instructor: Sunil Gupta
Product ID: 706569
Training Level: Basic to Intermediate

  • Duration: 90 Min
This presentation reviews the six levels of metdata level processing that all organizations need to be aware of and plan for. In the current days of automation in our daily lives at home, in the car and in various industries, FDA submissions in the pharma industry is next in line for more automation through metadata. We will explore examples for each of the metadata level. Metadata will be reviewed to understand how metadata has answers to key questions for logical processing. SAS tools will be reviewed to write code for more automation process.
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Why Should You Attend:

This course is intended for anyone directly or indirectly responsible for the creation, content or validation of CDISC data sets, tables, and data lists used to support research, drug or medical device efficacy and safety in a regulatory submission. Professionals in the pharmaceutical, biotechnology and medical device industries who want to be Good Clinical Practices (GCP) compliant in relation to regulatory submission environment will benefit from this unique course. Effective and practical solutions to address real-world issues will be provided.

CDISC requirements to create SDTMs and ADaMs are not easy to understand or apply. There are many rules and standards that must be mastered and maintained across global studies. Pharmaceutical companies and CROs supporting global studies have a need to apply proven methods that reduce confusion and improve documentation. With new members joining the study team, there should be a system to help standardize and automate the FDA submission process. SDTM QC

Seasoned SAS programmers know how to leverage SAS for data science applications by taking advantage of metadata and SAS macro programming. Accessing metadata of datasets and SAS programs to automate and standardize processing of multiple files can be achieved without manual efforts. Combined with SAS macro programming, automation allows for more robust and advanced programming techniques to handle messy data as well as unexpected changes in data input. In a dynamic environment, this helps to assure confidence in processing large number and amount of data to convert data into meaningful information and action items.

Data Transfer Compliance

Since data is constantly transferred from vendors with different schedules based on the type of data, organizations must be proactive to control the acceptance of each data transfer. With each data transfer, organizations must be confident that all data received is correct by confirming full scope of all variables, attributes and records against the data transfer specifications. Often, missing variables or data truncation can occur due to faulty coding or values being set to missing.

The examples in this presentation are essential clinical data metadata for effective data transfers. The dictionary datasets, TABLES and COLUMNS, contain much of this information and can easily be accessed. Any metadata information outside of TABLES and COLUMNS data sets can be created as customized checks using SAS.

Learning Objectives:

Upon completion of this training, you will be able to:

  • Better understand how SDTM and ADaM metadata play an important role to automate the process
  • Know differences between standard and custom metadata compliance checks
  • Create higher data quality and high compliance SDTMs and ADaMs

Areas Covered in the Webinar:

  • Six Levels of Metadata Processing
  • Information to apply for making programming decisions
  • Process large volume of data from multiple sources
  • Standard vs Custom Metadata
  • Automate administrative tasks
  • Compliance Checks
  • Alerts for more active monitoring of data issues and milestones
  • Risks and drawbacks
  • New Data: Duplicates, Holes and Invalid Values
  • Data Transfer Compliance Gate
  • SAS LSAF and Data Management Tools
  • Proc SQL to Build Custom Metadata
  • SAS: Extract intelligence information
  • 8 Standard and Custom Metadata Examples

Who Will Benefit:

  • SAS Statistical Programmers
  • Quality Assurance Specialists
  • SAS Statistical Managers
  • Medical Writers
  • Statisticians
  • Regulatory Affairs Associates
  • Clinical Data Managers
  • Directors, Statistical Programming
  • CRO Professionals
  • Health Care Professionals
  • Research University Specialists

Free Materials related to the topic:

  • Reference documents
  • Rule documents or guidance
  • Checklist
  • SOP template
  • Easy fill in forms
  • Articles

Frequently asked question and answer:

  • Q. Why is it important to check for data transfer compliance? A. To prevent downstream errors and assure quality new data
  • Q. Along with duplicates, missing and invalid values, are there other good compliance checks to perform? A. Consistency of new data and preventing loss of data
  • Q. Do I need to be a Proc SQL expert to create custom metadata? A. Ideally yes to take full advantage of creating most any summary information and saving in macro variables for example
  • Q. What approach do you recommend for leveraging metadata? A. Start by understanding and working with the dictionary tables to review dataset and variable attributes
  • Q. Can I create my own custom metadata checks? A. Yes, see the proc sql examples in this presentation
Instructor Profile:
Sunil Gupta

Sunil Gupta
SAS Global Forum Conference Presenter Mentor, SAS

Sunil Gupta is an international speaker, best-selling SAS author, and a global corporate trainer. He is the principal SAS/CDISC consultant at Gupta Programming since 1994. Most recently, he taught both of his CDISC online classes with the University of California at San Diego and SAS Institute India. Mr. Gupta will start to teach the new Sharpening Your SAS Skills online class for UCLA Extension in 2016. In 2011, he launched his unique SAS resource blog, SASSavvy.com, for smarter SAS searches. Currently, SAS Savvy’s membership consists mostly of SAS programmers, university students and pharmaceutical corporate accounts.

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