This training program will assist 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 training. Effective and practical solutions to address real-world issues will be detailed.
This webinar will enumerate essential mapping and strategy concepts for creating and validating SDTM and ADaM variables in key CDISC datasets (DM, AE, ADSL, and ADAE). Examples of both SDTM and ADaM dataset structures will be reviewed and compared. In addition, a mapping plan from raw datasets to SDTM to ADaM datasets will also be outlined. To help assure higher quality clinical data, a QC checklist and some key edit check macros will be introduced.
Participants will receive a copy of the new CDSIC e-guide and all SAS macros reviewed in class. Through case study analysis, the course will examine best practices to provide thoughts and ideas to develop or improve the CDISC mapping system.
Why Should You Attend:
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.This webinar will enumerate essential mapping and strategy concepts for creating and validating SDTM and ADaM variables in key CDISC datasets (DM, AE, ADSL, and ADAE). Examples of both SDTM and ADaM dataset structures will be reviewed and compared. In addition, a mapping plan from raw datasets to SDTM to ADaM datasets will also be outlined. To help assure higher quality clinical data, a QC checklist and some key edit check macros will be introduced.
Participants will receive a copy of the new CDSIC e-guide and all SAS macros reviewed in class. Through case study analysis, the course will examine best practices to provide thoughts and ideas to develop or improve the CDISC mapping system.
Learning Objectives:
- Utilize metadata to automatically assign variable attributes in SDTMs and ADaMs
- Understand the differences between the four different variable roles and three different variable types
- Better understand the nine steps to SDTM mapping and seven steps to ADaM mapping
- Better understand raw data, dataset joins and traceability concepts
Areas Covered in the Webinar:
- Compare SDTM and ADaM - Key Differences
- When are Key SDTM/ADaM Variables Created
- Mapping Raw Data to SDTMs by SDTMs
- Nine Step SDTM Mapping Plan
- Confirm Date Variables
- SDTM Mapping of Study Day, ex AESTDY
- Three Types of Raw Data Collected
- Three Types of Dataset Joins
- Match DM Variables: Three Types of Raw Variable Mapping to SDTMs
Seven Step ADaM Mapping Plan
- ADaM Model Concepts
- ADaM Six Levels of Flag Variables
- ADaM BDS Variable Types
- Three ADaM Models
- Traceability
- ADaM Mapping Plan - Analysis visit windows
- ADLB - DTYPE=‘XXX’ New Records
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
Course Provider
Sunil Gupta,
