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Advanced SAS Clinical with CDISC, SDTM, and ADAM Course

This course teaches SAS programmers essential concepts about creating and validating SDTM and ADaM variables in key CDISC datasets. You will learn how to build and process hierarchy of adverse events variables and paired lab variables.

Course syllabus
Price: $400 - $500
  • Introduction to CDISC
  • Introduction to SDTM
  • ADaM introduction
  • Base SAS Programming

About Advanced SAS Clinical With CDISC, SDTM And ADAM

  • Clinical Data Interchange Standards Consortium (CDISC) exists to develop and support global platform independent standards for improving data quality and accelerate the process. These standards are useful for packaging, reporting and streamlining clinical research data. CDISC enables the electronic acquisition, submission, exchange and archiving of clinical trials data for improving medical research, product development, and other related medical healthcare areas.

    Study Data Tabulation Model (SDTM) provides standards for regulatory submission of case report from data tabulations produced in clinical research studies.

    Analysis Data Model (ADaM) provides the content standard for the organization, structure, and format of analysis datasets and related metadata.

    SDTM holds Raw data, and Adam holds analysis datasets. The efficient way of deploying is creating SDTM datasets and then creating ADaM datasets and then finally display the outputs in the form of tables, figure, and listings based on the ADaM datasets.

Advanced SAS Clinical With CDISC, SDTM And ADAM Course Content

  • Why CDISC, SDTM, and ADAM?

    CDISC is very popular because it provides a standardized dataset names and layout, supports standardized variable naming conventions and enables standardized calculations for common variables and percent change from baseline.

    The advantage of using ADaM is, it supports efficient generation, replication, and review of clinical trial statistical analyses. ADaM also provides traceability between the analysis results, analysis data, and data represented in the Study Data Tabulation Model (SDTM).

    SDTM data is standardized, standardized programs and codes from which you can derive ADaM data, leading to increased efficiency in the process.

  • What will I learn?

    • Creating CDISC SDTM datasets for domains from raw datasets
    • Preparing ADaM datasets
    • Creating SDTM Domains
    • Drafting Tables, Listings, Figures/Graphs (TLF/TLGs)
    • Understanding SAS datasets
    • Using SAS Macros and SAS programs to transform raw data into SDTM data for statistical analysis and submission
    • Understand how SDTM and ADaM metadata play a significant role to automate the process
    • Know how to maintain control terminology and value level metadata
  • Requirements

    • Minimum Graduate Degree in Life Sciences
    • Minimum Graduate Degree in Statistics
    • Degree in any of these fields MBBS, BDS, BHMS, and BAMS
  • Audience

    • Data manager
    • Clinical Programmer
    • Statistical Programmer
    • Bio-statistician, or a clinician

Advanced SAS Clinical With CDISC, SDTM And ADAM Course Syllabus

  • Introduction to CDISC

    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
  • Introduction to SDTM

    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
  • ADaM introduction

    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

    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
  • SDTM & ADaM Assignments

    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
  • SDTM Metadata

    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
  • Base SAS Programming

    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
  • Tables, Listings, Figures/Graphs (TLF/TLGs) by using SAS Programming

    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

    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

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