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CDISC-SDTM

IGCP Training Institute is one of the Best CDISC Training Institute in Hyderabad. It provides various industrial Training Courses in Hyderabad, like CDISC, Clinical Research, Clinical Data Management, SAS, etc. Pharmacovigilance with Database (EDC) Inform Architecture, Central Designer for pharmacy students and life science, health science graduates, and postgraduates. Trained students of CDISC through the IGCP Training Institute, Hyderabad gives full support to every medical research of any type from protocol by analyzing the progress and interpreting their results. Students trained from IGCP Training Institute Hyderabad have shown that they can reduce the resources by 70–90% in the start-up stages at the start of their research process, which is almost 60%.

The Clinical Data Interchange Standards Consortium (CDISC) is formed in 1997 to develop and support global platform-independent data standards. CDISC has brought many global standards and innovations in medical research by ensuring a secure link with healthcare. IGCP Training Institute offers Training Classes for aspiring professionals who are wanting to pursue their career in CDISC.

The CDISC Training Course from IGCP, Hyderabad, provides a bright career path for professionals.

IGCP Institute Hyderabad has highly experienced staff for CDISC Training Course, Clinical Research, and Data Management students, making the first choice of people to choose the IGCP Institute. IGCP Training Institute Hyderabad provides job-oriented real-time training with practical knowledge to students to make their understanding better and fully grasp the subject. Our faculty is available 24*7 for the classroom and lab to remedy every single doubt of the aspirant.

The IGCP Hyderabad provides the best CDISC Training Course because of the following reasons:

● Real-time Projects

The Live Projects are based on the selected Domains to provide the best user experience to the learners.

● Lab Sessions and Assignments

In CDISC Training Courses by IGCP, the students will be given Online Lab sessions for practical knowledge, followed by practical assignments for better understanding.

● 24 x 7 Expert Support

To solve our students' queries, we provide a 24x7 support team to our students for any technical questions of students during the course undergoing the CDISC Training Course through IGCP Hyderabad.

● Live Sessions

The students will learn better through Online Live Classrooms, so IGCP Hyderabad also provides Online CDISC Courses.

● Certification

After the competition of this course, the students will be allocated projects. After completing these projects, the Experts analyze the projects, and accordingly, certificates are disturbed to the students.

COURSES WE OFFER CDISC-SDTM

  • Introduction
  • Fundamentals of SDTM
  • Submitting Data in Standard Format
  • Assumption for Domain Models

STUDY DATA TABULATION MODEL (SDTM):

  • INTRODUCTION TO SDTM
  • CRF ANNOTATION
  • MAPPING SPECIFICATIONS
  • SDTM PROGRAMMING

SUBMITTING DATA IN STANDARD FORMAT:

  • Standard Metadata for Dataset Contents and Attributes
  • Using the CDISC Domain Models in Regulatory Submissions - Dataset METADATA
  • Primary Keys
  • CDISC Submission Value-Level Metadata
  • conformance
  • MODELS FOR SPECIAL-PURPOSE DOMAINS:

    • Demographics
    • Comments
    • Subject Elements
    • Subject Visits

    DOMAIN MODELS BASED ON THE GENERAL OBSERVATION CLASSES:

    • Interventions
    • Events
    • Findings
    • Findings about

    TRIAL DESIGN DATA SETS:

    • Introduction
    • Trial Arms
    • Trial Elements
    • Trial Visits
    • Trial Inclusion/Exclusion Criteria
    • Trial Summary Information
    • How to Model the Design of a Clinical Trial

    REPRESENTING RELATIONSHIPS AND DATA:

    • Relating Group of Records Within a Domain Using the Grpid Variable
    • Relating Peer Records
    • Relating Datasets
    • Relating Non-Standard Variables Values to a Parent Domain
    • Relating Comments to A Parent Domain
    • How to Determine where Data Belong in the SDTM
    • Trial Design Datasets
    • Representing Relationships And Data
    • Models for Special Purpose Domains
    • Domain Models Based on General Observation Classes

    FUNDAMENTALS OF THE SDTM:

    • Observations and Variables
    • Datasets and Domains
    • Special-Purpose Datasets
    • The General Observation Classes
    • The SDTM Standard Domain Models
    • Creating a New Domain

ASSUMPTIONS FOR DOMAIN MODELS:
GENERAL ASSUMPTIONS FOR ALL DOMAINS:

  • General Domain Assumptions
  • Review Study Data Tabulation and Implementation Guide
  • Relationship to Analysis Datasets
  • Additional Timing Variables
  • Order of the Variables
  • CDISC Core Variables
  • Additional Guidance on Dataset Naming
  • Splitting Domains
  • Origin Metadata
  • Assigning Natural Keys in the Metadata

GENERAL VARIABLE ASSUMPTIONS:

  • Variable-Naming Conventions
  • Two-Character Domain Identifier
  • Use of "Subject" and USUBJID
  • Case Use of Text in Submitted Data
  • Grouping Variables and Categorization
  • Submitting Free Text from the CRF
  • Multiple Values for a Variable

CODING AND CONTROLLED TERMINOLOGY ASSUMPTIONS:

  • Types of Controlled Terminology
  • Controlled Terminology Text Case
  • Controlled Terminology Values
  • Use of Controlled Terminology and Arbitrary Number Codes
  • Storing Controlled Terminology for Synonym Qualifier Variables
  • Storing Topic Variables for General Domain Models
  • Use of "Yes" and "No" Values

ACTUAL AND RELATIVE TIME ASSUMPTIONS:

  • Formats for Date/Time Variables
  • Formats for Date/Time Variables
  • Intervals of Time and Use of Duration for -DUR Variables
  • Use of the "Study Day" Variables
  • Clinical Encounters and Visits
  • Representing Additional Study Days
  • Use of Relative Timing Variables
  • Date and Time Reported in a Domain Based on Findings
  • Use of Dates as Result Variables
  • Representing Time Points

OTHER ASSUMPTIONS:

  • Original and Standardized Results of Findings and Tests Not Done
  • Linking of Multiple Observations
  • Text Strings That Exceed the Maximum Length for General-Observation-Class Domain Variables
  • Evaluators in the Interventions and Events Observation Classes
  • Clinical Significance for Findings Observation Class Data
  • Supplemental Reason Variables
  • Presence or Absence of Pre-Specified Interventions and Events

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Institute Of Good Clinical Practice

Hyderabad

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+91 9030070095,

+91 8790209568.