Statistical Analysis Software (SAS) is used in clinical trials for data management, advanced analytics, and predictive analysis, with results standardized for FDA submissions using CDISC guidelines. The course covers both basic and advanced SAS programming, clinical research processes, data management practices, and the creation of statistical datasets for regulatory compliance. SAS programmers play a crucial role in enhancing patient care and meeting healthcare industry needs, particularly in multinational pharmaceutical companies.

1. (CLINICALSAS)

2. INTRODUCTION
Statistical Analysis Software(SAS) :is a Analytical Tool
 Developed by SAS institute( At North Carolina State
University) for:
 Data Management
 Advance Analytics
 Multivariate Analysis
 Business Intelligence
 Criminal Investigation and
 Predictive Analysis.

3. CLINICAL SAS
 Clinical Trial SAS programming (Clinical SAS):is a Research
Oriented programming where
 the Raw research Data Collected during the clinical Trials is
Accessed, explored, cleaned, Analyzed ,Transformed and
exported in different formats(PDF, EXCEL,RTF,CSV)
 the Analysis results are displayed in the form of Tables, Listings,
Reports and graphs.
 the final data is standardized as per CDISC Standards using SDTM,
ADAM, DEFINE implementation guides.
 The standardized Datasets are submitted to the FDA for Approval of
New Drugs.

4. COURSE DETAILS
Clinical SAS is basically divided into :
 BASE AND ADVANCE SAS
 CLINICAL RESEARCH & CDM (Clinical Data
Management)
 CDISC-SDTM ADAM & TLFS(Tables, Listings,
Figures) and GRAPHS

5. BASE SAS &ADVANCE SAS
 It includes the Basic SAS programming Concepts which includes:
 Creating New Data Sets using Data step.
 Accessing the data Through Libraries into SAS.
 Importing data into SAS environment using INFILE and PROC
IMPORT.
 Exploring the Data using PROC PRINT, PROC FREQ, PROC
MEANS and PROC UNIVARIATE.
 Filtering The data using WHERE Expression and Macro Variables.

6.  Formatting ,Sorting the Data and Removing duplicate rows
using Formats and PROC SORT.
 Computing New Columns using Numeric, Character and
other functions.
 Conditional processing of Data using IF-THEN,IF-THEN-
ELSE, DO , DO-WHILE, DO-UNTIL loops.
 Analyzing and Reporting the Data using PROC FREQ
,PROC MEANS and PROC REPORT.
 Exporting the results in differents Formats
(Excel,RTF,PDF,CSV)using ODS(Output Delivery System).

7. CLINICAL RESEARCH & CDM
 Clinical research is a branch of healthcare science that
determines the safety and efficacy of medications, devices,
diagnostic products and treatment regimens intended for human
use.
 Clinical Data Management(CDM): is a critical process
in clinical research, which leads to generation of high-quality,
reliable, and statistically sound data from clinical trials.
 Clinical data management ensures collection, integration and
availability of data at appropriate quality and cost.

8. ADVANCE SAS
 MACROS:
 Defining Macros, Creating Macro Variables.
 Arimetic Calculation in Macros using eval and sysevalf.
 Using SAS procedures in Macros.
 Functions ,Conditional processing, Iterative loops in
Macros.
 Creating Macros in data step by call symput.
 Use of macros in Titles and Footnote.
 Macro debugging options such as
Symbolgen,mprint,mlogic.

9.  SQL:
 Creating tables, alter and Update table using PROC
SQL.
 Renaming and Labelling Columns.
 Filtering data using WHERE expression.
 Numeric, Aggregate and String functions in SQL.
 Creating macros in SQL.
 Grouping and Ordering data in SQL.
 Joining tables by SET OPERATORS or JOINS.

10. In Clinical Research We are going to study the following:
 Clinical Research process: Divided basically into Phases Pre-Clinical,
Phase I,
Phase II, Phase III, Phase IV.
 IRB/IEC, FDA and ICH.
 Sponsor
 Investigator & Investigator Brochure.
 Clinical Trial Design.
 CRO(Contract Research Organization).
 Clinical Trial Protocol
 Randomization.
 Case Report Form(CRF).

11.  In CDM we are going to study:
 Electronic Data Capture.
 Creating Database, Database Validation ,Programming and Standards.
 Data Entry Process.
 Data Storage.
 Medical Coding Dictionaries(MEDDRA).
 Safety Data Management And Reporting
 Serious Adverse Events Data Reconciliation
 Database Closure or Database Lock.

12. TLFS AND GRAPHS
 Tables,Listings and Figures plays vital role in SAS programming to
display the data in a readable format.
 To display the data in the form of Charts and Graphs which helps in
Data Analysis.
 To display the data as per SAP(statistical Analysis Plan) document
which is prepared to assist SAS programmers to detail on the scope of
planned analyses, population definitions, and methodology on how
prospective decisions are to be made for presenting study results.
 TLF’s helps in submission of Final document to FDA And sponsors.
 Graphs are prepared using procedures such as PROC CHART, PROC
PLOT, PROC GCHART, PROC GPLOT,PROC SGPLOT.
 Tables as per SAP are prepared using PROC SORT, PROC FORMAT,
PROC TRANSPOSE, and PROC REPORT.

13. SDTM
 Study Data Tabulation Model Implementation Guide for Human Clinical Trials
(SDTMIG)
 Prepared by the Submissions Data Standards (SDS) team of the Clinical Data
Interchange Standards Consortium (CDISC).
 To guide the organization, structure, and format of standard clinical trial
tabulation datasets submitted to a regulatory authority (FDA).
 Standardized datasets helps
1. To store all submitted data in a repository and
2. With the use of standard software tools, help to work with the data more
effectively with less preparation time and better support viewing and
analysis.
3. Facilitate data interchange between partners and providers.
 SDTM represents an interchange standard.

14. In SDTMIG We will Study:
 Introduction To SDTM.
 Fundamentals of the SDTM
 Submitting Data in Standard Format
 Assumptions for Domain Models
 Models for Special-Purpose Domains
 Domain Models Based on the General Observation
Classes(interventions, Events and Findings)
 Trial Design Domains.
 Representing Relationships and Data

15. ADAM
 Analysis Data Model Implementation Guide (ADaMIG):
 prepared by the Analysis Data Model (ADaM) Team of CDISC .
 Both the SDTM and ADaM standards were designed for submission to a
regulatory agency (FDA).
 It describes fundamental principles that apply to all analysis datasets.
 the design of ADaM datasets and associated metadata facilitate explicit
communication of the content and source of the datasets supporting the
statistical analyses .
 The Analysis Data Model supports efficient generation, replication, and
review of analysis results.

16. The ADaMIG specifies :
 ADaM standard dataset structures ,variables and naming conventions.
 It also specifies standard solutions to implementation issues.
In ADAMIG we are going to study:
 Introduction to ADAMIG.
 Fundamentals of the ADaM Standard.
 There are two ADaM standard data structures:
1. Subject-Level Analysis Dataset (ADSL) and 2. Basic Data Structure (BDS).
 Standard ADaM Variables:
1. ADSL variables and 2. BDS Variables
 Implementation Issues, Standard Solutions, and Examples.

17. SCOPE
 SAS programmers play an important role in clinical trial data analysis.
 The opportunities for SAS programmers to address the technical needs of
the healthcare industry are ever expanding.
 SAS can help meet healthcare professionals meet business goals, control
costs, generate greater revenue and enhance strategic performance
management.
 Clinical SAS provides a number of analytical tools that are used to explore
drug result and risk tolerances to improve the quality of patient care.
 SAS play a major role in clinical trials, right from defining the clinical study
to till regulatory submission.
 Multinational Companies like Novartis, Glaxo Smithkline, Pfizer, Roche
Have a separate Division for Research and Development where there is a lot of
scope for clinical SAS programmers.