This lesson teaches about CDISC that helps to structure the data and specifies the data to for collection and the method to conduct clinical trials, assessments or endpoints. The advantage of using CDISC is that it facilitates data sharing, increases predictability, improves data quality, streamlines the process and reduces costs.

Class 1.1:

Introduction of CDISC
Why CDISC and DATA standards
CDISC Versions
Impact of CDISC Standards on Clinical Activities
CDISC Models

SDTM defines a standard structure for human clinical trial data tabulations and nonclinical study data tabulations. In this session, you will learn to create new domains.

Class 2.1:

What is SDTM?
Observations and Variables in SDTM
Special Purpose Datasets

Class 2.2:

General Observation Classes in SDTM
SDTM Standard Domain Models
Creating New Domain

The Analysis Data Model identifies the principles for analysis datasets and standards for a subject-level analysis file and a basic data structure that you can use for a wide variety of analysis methods.

Class 3.1:

Overview of ADaM
ADaM Data Structure
ADaM Metadata
Developing Dataset
Mapping specs creation

Metadata plays a vital role for successful automation. The Programs written using SAS read the table-driven metadata to translate the analysis data into SDTM formats. Metadata instructs the SAS code about which study variables populates the SDTM variables. All code developed are generic using the metadata to indicate when variations are required.

Class 4.1:

Variable level Metadata
Codelist Metadata
 Value-Level Metadata
Computational Method Metadata
Define File Header Metadata

The SDTM and ADaM dataset development are independent, and they can complete at any time without input from the other. The independence of the SDTM and ADaM datasets allows you to parallelly work with the extraction, transformation and loading the(ETL) processes.

Class 5.1:

SDTM to ADaM mapping
Traceability reporting
Standardized ETL code

This lesson explains about the Metadata which talks all about the datasets. Datasets are the variables used or the values of the variables. Complete and accurate metadata is essential in the clinical space for submission and approval of drugs and devices.

Class 6.1:

Developing Dataset
Validating Dataset
Mapping specs creation

SAS Programming lets you process the DBMS extract to the SDTM domains. You can build conventional DSMS structure, standard mappings and standard extracts using the SAS software.

The advantage of using Base SAS software is that it allows you to take complete control over the domain mappings and provides documentation in the form of SAS programs and log files. The SAS code in the SDTM increases the programming efficiency and also streamlines the documentation.

Class 7.1:

Introduction to SAS Programming
SAS Essentials and Techniques
SAS Datasets
Healthcare data and SAS

Researchers and programmers use SAS to analyze, summarize, and report clinical trial data. This lesson teaches you the method to create and implement the tables, listings, and graphs using SAS

Class 8.1:

Creating A List Report
Tabulate Procedure
One-Dimensional Tables
Two-Dimensional Tables

Class 8.2:

Graphs Overview
Gchart Procedure
Plots

Implementing CDISC SDTM with SAS allows you to submit animal and human study datasets in electronic format. Datasets are a display of the study data used by reviewers to conduct specific analyses of the study data. They include both raw and derived data.

Class 9.1:
Study Populations or Analysis Populations
Study day Variable
Change from Baseline
Visit Windowing

Class 9.2:
Basic Lab Pre-Processing
Dealing with Partial Dates
Time to Event Analysis
Duration of Response

Class 9.3:
Determining value for VISFWDID in Adverse Events
Transposing Datasets
Flagging Concomitant Medications