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SAS Clinical Data Management

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72 hours
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Why Join Handson for this Course?

Through interactive exercises, you can learn and crack the most popular job-relevant questions.

One to One mentoring with dedicated career mentor

Learn to work with the versatile subject for scripting research applications and much more.

Master the various activities and methodology used by SAS Programmers.

Develop hands-on skills using the most popular tools used by the experts.

Work with real-world datasets to enable stakeholders draw informed conclusions.

Program Overview

This course is for users who want to learn how to write SAS programs apply in Clinical Research. It is the entry point to learning SAS programming and is a prerequisite to many other SAS courses. The course is designed for candidates who aspires to build career in the Pharma and Clinical World.

Key Features

Program Advantage

A comprehensive Program – covering complete knowledge on Data manipulation techniques, SAS Macro language, Clinical Data Management, SAS SQL, Advanced techniques and Efficiencies. The Course is designed and Structured to meet the demands of the Industry and career aspirations of the students.


Base SAS Programming
SAS Programming 1: Essentials

▪ the SAS programming process
▪ using SAS programming tools
▪ understanding SAS syntax

▪ understanding SAS data
▪ accessing data through libraries
▪ importing data into SAS

▪ exploring data
▪ filtering rows
▪ formatting columns
▪ sorting data and removing duplicates

▪ reading and filtering data
▪ computing new columns
▪ conditional processing

▪ enhancing reports with titles, footnotes, and labels
▪ creating frequency reports
▪ creating summary statistics reports

▪ exporting data
▪ exporting reports

▪ using Structured Query Language in SAS
▪ joining tables using SQL in SAS

SAS Programming 2: Data Manipulation Techniques

▪ setting up for this course
▪ understanding DATA step processing
▪ directing DATA step output

▪ creating an accumulating column
▪ processing data in groups

▪ understanding SAS functions and CALL routines
▪ using numeric and date functions
▪ using character functions
▪ using special functions to convert column type

▪ creating and using custom formats
▪ creating custom formats from tables

▪ concatenating tables
▪ merging tables
▪ identifying matching and nonmatching rows

▪ using iterative DO loops
▪ using conditional DO loops

▪ restructuring data with the DATA step
▪ restructuring data with the TRANSPOSE procedure

SAS Macro Language 1: Essentials

▪ overview of SAS Foundation
▪ course logistics
▪ course data files
▪ purpose of the macro facility
▪ program flow

▪ introduction to macro variables
▪ automatic macro variables
▪ macro variable references
▪ user-defined macro variables
▪ delimiting macro variable references
▪ macro functions

▪ defining and calling a macro
▪ macro parameters

▪ creating macro variables in the DATA step
▪ indirect references to macro variables
▪ creating macro variables in SQL

▪ conditional processing
▪ parameter validation
▪ iterative processing
▪ global and local symbol tables

▪ SAS resources
▪ beyond this course

▪ program flow

SAS SQL 1: Essential

▪ overview of SAS Foundation
▪ course logistics
▪ course data files
▪ introducing the Structured Query Language

▪ overview of the SQL procedure
▪ specifying columns
▪ specifying rows

▪ presenting data
▪ summarizing data

▪ introduction to SQL joins
▪ inner joins
▪ outer joins
▪ complex SQL joins

▪ noncorrelated subqueries
▪ in-line views

▪ introduction to set operators
▪ the UNION operator
▪ the OUTER UNION operator
▪ the EXCEPT operator
▪ the INTERSECT operator

▪ creating tables with the SQL procedure
▪ creating views with the SQL procedure

▪ dictionary tables and views
▪ using SQL procedure options
▪ interfacing PROC SQL with the macro language

▪ SAS resources
▪ beyond this course

SAS Programming 3: Advanced Techniques and Efficiencies

▪ overview of SAS Foundation
▪ course logistics
▪ creating the course data

▪ identifying computer resources related to efficiency

▪ SAS DATA step processing
▪ controlling I/O
▪ reducing the length of numeric variables
▪ compressing SAS data sets
▪ using SAS views

▪ access methods
▪ accessing observations by number
▪ creating an index
▪ using an index

▪ introduction to lookup techniques
▪ one-dimensional arrays
▪ multidimensional arrays
▪ loading a multidimensional array from a SAS data set

▪ introduction
▪ hash object methods
▪ loading a hash object from a SAS data set
▪ DATA step hiter object

▪ DATA step merges and SQL procedure joins
▪ using an index to combine data
▪ combining summary and detail data
▪ combining data conditionally

▪ user-defined functions
▪ user-defined formats

▪ areas of support from SAS
▪ other courses to consider

▪ combining raw data files vertically

SAS Clinical Program
SAS/GRAPH 1: Essentials

▪ course logistics
▪ graphical reporting overview

▪ graph types produced by SAS/GRAPH
▪ SAS/GRAPH environment
▪ SAS/GRAPH program structure
▪ SAS/GRAPH and the Output Delivery System

▪ creating scatter plots
▪ creating line plots
▪ creating plots containing multiple lines
▪ creating other types of plots with individual data points
▪ creating other types of plots with grouped data (self-study)

▪ creating vertical and horizontal bar charts
▪ creating grouped and subgrouped bar charts

▪ creating block charts (self-study)
▪ creating bar-line charts
▪ creating area bar charts

▪ creating pie charts
▪ creating donut charts (self-study)
▪ creating star charts (self study)
▪ creating radar charts
▪ creating tile charts
▪ creating key performance indicator charts

▪ customizing plot and chart axes
▪ customizing axes with AXIS statements
▪ customizing legends

▪ common attributes of graphics elements
▪ customizing common graphics elements
▪ customizing plot appearance
▪ customizing chart appearance
▪ customizing axis appearance

▪ creating basic annotations
▪ creating data-dependent annotations

▪ creating image files
▪ creating client-rendered graphs
▪ creating clickable graphs for the Web using ODS

▪ storing and naming graphics output
▪ using the GREPLAY procedure in line mode

SAS Report Writing 1: Using Procedures and ODS

▪ course logistics
▪ sending a report to an ODS destination

▪ using basic TABULATE procedure statements
▪ enhancing the table
▪ adding percentages
▪ more about picture formats (self-study)

▪ handling missing values with PROC TABULATE
▪ using ODS STYLE= options with PROC TABULATE
▪ working with pages and BY groups
▪ controlling row structure and data subsets
▪ using multilabel formats with PROC TABULATE
▪ working with PROC TABULATE and the listing destination

▪ using basic REPORT procedure statements
▪ adding summary lines
▪ computing an additional column
▪ working with PROC REPORT in the listing destination (self-study)

▪ defining and using group variables
▪ customizing break lines
▪ defining and using across variables

▪ using absolute column names with ACROSS usage
▪ working with missing values and PROC REPORT
▪ working with STYLE= overrides with PROC REPORT
▪ enhancements using the CALL DEFINE statement (self-study)
▪ advanced compute block examples (self-study)

▪ adding options to ODS destination statements
▪ using additional ODS features (self-study)
▪ using cascading style sheets with ODS (self-study)

▪ attributes for use with the STYLE= options in PROC REPORT and PROC TABULATE

Processing Database and Spreadsheet Data with SAS/ACCESS Software

▪ understanding databases
▪ establishing the requirements to connect to a database
▪ establishing the requirements to connect to an Excel workbook

▪ connecting to a database table using SAS/ACCESS LIBNAME engines
▪ connecting to an Excel workbook
▪ explaining and applying Open Database Connectivity (ODBC)
▪ using an embedded LIBNAME statement

▪ passing queries to your database management system (DBMS)
▪ passing non-queries to your DBMS
▪ comparing the SQL Pass-Through Facility and the SAS/ACCESS LIBNAMEengines

▪ combining tables using a DATA step merge
▪ joining tables using the SQL Pass-Through Facility and the SQL procedure
▪ joining tables from different databases

▪ Using the IMPORT procedure to read a Microsoft Access table and an Excel worksheet into a
SAS data set
▪ Using the Export Wizard to write a SAS data set and an Oracle table to an Excel workbook
▪ Using the EXPORT procedure to export a SAS data set to an Excel workbook

▪ creating and updating an access descriptor
▪ creating and updating a view descriptor

SAS Clinical Data Integration: Essentials

▪ purpose and functions of SAS Clinical Data Integration
▪ understanding the software components comprising SAS Clinical Data Integration
▪ overview of SAS Clinical Data Integration components in SAS Data Integration Studio

▪ Clinical Data Integration case study description
▪ overview of the domain loading jobs
▪ defining source table metadata
▪ defining target domain metadata

▪ introduction
▪ loading the DM (Demographics) domain
▪ loading the XP (Pain Diary) domain
▪ loading the SUPPDM (Supplemental Demographics) domain
▪ loading the QS (Questionnaire) domain

▪ introduction to CDISC-SDTM compliance checks
▪ compliance checks for the DM domain
▪ compliance checks for the QS domain
▪ compliance checks for an externally supplied domain

▪ creating a standard CRT-DDS Define.xml document
▪ creating customized CRT-DDS Define.xml documents

▪ importing standard domains, domain columns, and compliance check metadata
▪ customizing a standard domain
▪ analyzing domain usage and promotion of custom domains
▪ creating customized compliance checks
▪ importing controlled terminology

▪ creating and modifying terminology packages
▪ creating default content
▪ creating a new clinical study

SAS Clinical Project Content (CDISC SDTM ADaM TLF)
  • Introduction to drug development process
  • Drug approval process
  • Introduction about Clinical trials process
  • Introduction of CDISC
  • Why CDISC and DATA standards
  • What are the versions of CDISC
  • Impact of CDISC Standards on Clinical Activities
  • CDISC Models
  • Study Data Tabulation Model (SDTM)
  • Analysis Dataset Models (ADaM)
  • Operational Data Model (ODM)
  • What is SDTM?
  • Observations and Variables in SDTM
  • Special Purpose Datasets
  • General Observation Classes in SDTM
  • SDTM Standard Domain Models
  •  DM(Demographics), CO(Comments), SE(Subjects Elements)
  • CM(Concomitant Medication), EX(exposure), SU(Substance Use), EC(Exposure as collected)
  • AE, MH
  • LB, EG, VS, PE, IE
  • TA, TE, TS, TI and TV
  • Supplemental Qualifies domains and relrec
  • SDTM Annotation on CRF – Concept



The application process consists of few simple steps. The  admission process will be completed after the payment is done by the selected candidates.

Submit Application

Submit the required documents for admission 

Application Review

Candidates will be selected based on their application


Selected candidates will get login credentials to begin the program


For admission to this program, candidates should have:

Any Graduate Candidate from any stream

Bachelor's degree with a minimum of 50% marks

Any aspiring Data Scientist in any functional area


We have a team, dedicated to solve your admissions related issues, who are available to guide you to apply for the program. They are available to:

Contact Us

+91 9830247087


We are dedicated to making our programs accessible and make it more economical.

Total Program Fee

₹ 36,000

(Incl. taxes)

For any fee related queries, please contact with our Admission Counselor in the above given number.

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