Clinical SAS Course in Ahmedabad

SAS (Statistical Analysis System) is used for data analysis and reporting in clinical trials organizations, pharmaceutical, biotech, and CRO (Clinical Research Organizations). SAS is the leader in today's analytics world. SAS is the industry standard for future advanced analytics in Clinical Research Analytics, helping Clinical SAS Programmers increase operational value and reduce risk.

SAS Clinical Trials programming is used to create and collect data sets for clinical research trials. SAS (Statistical Analysis System) provides a secure analysis foundation and scalable framework for clinical analysis and submission.

SAS provides a secure analysis foundation and scalable framework for clinical analysis and submission to pharmaceutical, healthcare, life science, and clinical research organizations.

SAS (Statistical Analysis System) is used in advanced analytics, business intelligence, data management, and predictive analytics, clinical trials management, clinical data management, clinical data analysis and reporting, drug safety data management, clinical supply, etc.

From a business perspective, SAS (Statistical Analysis System) can help healthcare organization professionals meet their business goals, control costs, achieve more revenue, and enhance strategic performance management.

If you are planning to learn SAS Certification Join the Best Clinical SAS Training Institute in Ahmedabad with Placement. If you are interested in pursuing a Clinical SAS Course in Ahmedabad then Aspire Techsoft Academy - SAS Authorized Training Partner in India is the right place to learn Clinical SAS Training in Ahmedabad.

If you needed Training on Clinical Trials like SDTM and ADAM CDIC. These are the most used tools in the CRO industry. India is the third-largest producer of pharmaceuticals in the world by volume.

So, After Bpharm or Mpharm - If you want to pursue a career in Clinical SAS then go ahead with Clinical Trials Which include SAS Base, Advance SAS, SAS Report, SAS Macros, and Statistics1. The combination of coursework and hands-on learning at Aspire Techsoft Academy the best Clinical SAS Training Institute in Ahmedabad helps you build a foundation of clinical research theory and data analysis skills - which can open doors to new opportunities.

To earn the Clinical Trials Programmer Professional Certification, you must first be a Certified SAS Base Programming Specialist or Advanced Programming Professional.

This Clinical SAS Training in Ahmedabad at Aspire Techsoft Academy helps you prepare for the following SAS Certified Professional: Clinical Trials Programming Using SAS 9.4. Aspire Techsoft Academy is the SAS Accredited Training Partner in India that offers Clinical SAS Certification Course in Ahmedabad. We provide quality training by globally accredited SAS Trainers.

Our SAS Clinical Trials Programming Online Course in Ahmedabad comprises SAS certification cost with Exam along with the best Clinical SAS Online Training.

SAS Accredited Training centers are part of the SAS Learning Partner community. Educational Program provides a standardized, supervised and systematic approach to learning.

Clinical SAS Certification Training in Ahmedabad with placement is offered by SAS Accredited Training Partner Aspire Techsoft Academy. At the same time, thousands of non-institutes are offering SAS Training, where proper facility, as well as quality, cannot be guaranteed. If you are specifically looking for a Clinical SAS Certification in Ahmedabad, no one but SAS can issue a global certificate.

Whatever the type of educational institution, it largely depends on the core strength and knowledge of the instructor.

Aspire Techsoft Academy is the top SAS Accredited Training center in India provide quality learning experiences to students and professionals. Our comprehensive SAS training courses help you enhance your skills, prepare for certification exams and achieve your career goals.

Clinical SAS involves programming using SAS software that is used to process all types of data related to clinical research. This includes data cleaning, data mining, and data analytics. Clinical research includes activities ranging from a clinical trial, clinical trial management, monitoring, reporting, Pharmacovigilance, and data management.

Clinical Trials Process Reports

Clinical Research Projects

Health Insurance Companies Data (Claims Data)

Electronic health records of patients

Tracking of Patients Statistics

Primary Data Sources - Patient Prescription History

FDA Research Data

Administrative Data

Health Surveys

Patient / Disease Registries

Patient Testimonials Data

Biotech, Life Science Company - Sales Data

Social Media & Website Records - Patient self-data according to their search health-related data

Clinical Trials modernization

Clinical trial operational analytics

Artificial Intelligence & Machine Learning

Clinical Data Transparency

Demonstrate knowledge of the clinical trials process and data structures.

Access, manage and transform clinical trials data.

Apply statistical procedures to analyze clinical trials data

Utilize macro programming for clinical trials data.

Report clinical trials results.

Validate clinical trials data reporting.

1. Writing SAS programs

2. Manipulating and summarizing data using SAS procedures.

3. Creating SAS programs that are reusable and dynamic using macros.

4. Processing SAS data using Structured Query Language (SQL).

5. Report writing essentials.

6. Creating high-quality presentation graphics.

7. Introduction to clinical trials and CRO industry

8. Hands-on training on study documents

9. Hands-on training on CDISC standards.

10. Deep dive into ADaM.

11. SDTM programming

12. ADaM programming.

13. Hands-on training in creating clinical trial reports.

14. Hands-on training creating clinical trial graphs

15. Hands-on training on Define.xml and Reviewer’s Guide.

16. Introduction to ANOVA, Regression, and Logistic Regression (free e-learning course).

17. Evaluation through assessments.

Programming Concepts for Base SAS, Proc SQL, and Macros

Clinical Trials Processes and Working Environment

Clinical Trial Data Management using Base SAS

Preparing and Classifying Clinical Trial Data

Implementing CDISC SDTM and ADaM Guidelines

Clinical Data Analysis using SAS REPORT and SAS GRAPH Procedures

Data Visualization Using SAS ODS Graphics

Performing Data Validation and Data Review

• Essentials

▪ the SAS programming process

▪ using SAS programming tools

▪ understanding SAS syntax

• Accessing Data

▪ understanding SAS data

▪ accessing data through libraries

▪ importing data into SAS

• Exploring and Validating Data

▪ exploring data

▪ filtering rows

▪ formatting columns

▪ sorting data and removing duplicates

• Preparing Data

▪ reading and filtering data

▪ computing new columns

▪ conditional processing

• Analyzing and Reporting on Data

▪ enhancing reports with titles, footnotes, and labels

▪ creating frequency reports

▪ creating summary statistics reports

• Exporting Results

▪ exporting data

▪ exporting reports

• Using SQL in SAS

▪ using Structured Query Language in SAS

▪ joining tables using SQL in SAS

• Controlling DATA Step Processing

▪ setting up for this course

▪ understanding DATA step processing

▪ directing DATA step output

• Summarizing Data

▪ creating an accumulating column

▪ processing data in groups

• Manipulating Data with Functions

▪ understanding SAS functions and CALL routines

▪ using numeric and date functions

▪ using character functions

▪ using special functions to convert column type

• Creating Custom Formats

▪ creating and using custom formats

▪ creating custom formats from tables

• Combining Tables

▪ concatenating tables

▪ merging tables

▪ identifying matching and nonmatching rows

• Processing Repetitive Code

▪ using iterative DO loops

▪ using conditional DO loops

• Restructuring Tables

▪ restructuring data with the DATA step

▪ restructuring data with the TRANSPOSE procedure

• Array Processing in Data Step

▪ Introduction to table Lookup Techniques

▪ One-Dimensional Arrays

▪ Multidimensional Arrays

▪ Loading a Multidimensional Array from a SAS Data set

▪ Environment and Guiding Principles

▪ Preparing Clinical Trial Data

▪ Classifying Clinical Trial Data

▪ Introduction to Clinical Data Interchange Standards Consortium (CDISC)

▪ Fundamentals of the Study Data Tabulation Model (SDTM)

• The General Observations Class

▪ Interventions Class

▪ Events Class

▪ Findings Class

• Datasets Other than General Observation

Models for Special-Purpose Domains

Trial Design Model (TDM) Datasets

Relationship Datasets

Study Reference Datasets

• Assumptions for Domain Models

• Base SAS Programs for SDTM Conversions

• Building SDTM Datasets

▪ Building the Special-Purpose DM and SUPPDM Domains

▪ Building the AE Events Domain

▪ Building the LB Finding Domain

▪ Fundamentals of ADaM

▪ Subject-Level Analysis Dataset (ADSL)

▪ Basic Data Structure (BDS)

▪ Building the Subject-Level Analysis Dataset (ADSL)

▪ Building the Adverse Events Analysis Dataset (ADAE)

• Introduction

▪ overview of SAS Foundation

▪ course logistics

▪ course data files

▪ introducing the Structured Query Language

• Basic Queries

▪ overview of the SQL procedure

▪ specifying columns

▪ specifying rows

• Displaying Query Results

▪ presenting data

▪ summarizing data

• SQL Joins

▪ introduction to SQL joins

▪ inner joins

▪ outer joins

▪ complex SQL joins

• Subqueries

▪ noncorrelated subqueries

▪ in-line views

• Set Operators

▪ introduction to set operators

▪ the UNION operator

▪ the OUTER UNION operator

▪ the EXCEPT operator

▪ the INTERSECT operator

• Creating Tables and Views

▪ creating tables with the SQL procedure

▪ creating views with the SQL procedure

• Advanced PROC SQL Features

▪ using SQL procedure options

▪ interfacing PROC SQL with the macro language

• Learning More

▪ SAS resources

▪ beyond this course

• Introduction

▪ course logistics

▪ purpose of the macro facility

▪ program flow

• Macro Variables

▪ introduction to macro variables

▪ automatic macro variables

▪ macro variable references

▪ user-defined macro variables

▪ delimiting macro variable references

▪ macro functions

• Macro Definitions

▪ defining and calling a macro

▪ macro parameters

• DATA Step and SQL Interfaces

▪ creating macro variables in the DATA step

▪ indirect references to macro variables

▪ creating macro variables in SQL

• Macro Programs

▪ conditional processing

▪ parameter validation

▪ iterative processing

▪ global and local symbol tables

• Learning More

▪ SAS resources

▪ beyond this course

• Supplemental Materials

▪ program flow

• Use SQL Procedure to create Data sets

▪ Use CREATE TABLE query to create Study Tabulations and Analysis Datasets

▪ Use Join and Union clauses to combine multiple tables

• Use Macro Programming to automate tasks

▪ Create a macro to derive ISO8601 date string from SAS date or datetime component variables

▪ Create a macro to derive SDTM study day variable

▪ Create a macro to calculate change from baseline

• Introduction to Reporting and ODS

▪ Course logistics

▪ Introduction to reporting.

▪ Introduction to ODS.

▪ ODS with the SAS interfaces.

▪ Creating reports with the PRINT procedure

• Getting Started with the TABULATE Procedure

▪ Introduction to the TABULATE procedure.

▪ Creating the structure of the table.

▪ Adding overall summarizations.

▪ Adding additional statistics.

• Doing More with the TABULATE Procedure

▪ Enhancing with labels and formats.

▪ Enhancing with options.

▪ Adding trafficlighting.

▪ Adding additional style attributes.

• Getting Started with the REPORT Procedure

▪ Introduction to the REPORT procedure.

▪ Creating a detail report.

▪ Creating a summary report with the GROUP usage.

▪ Creating a summary report with the ACROSS usage.

▪ Computing an additional column.

• Doing More with the REPORT Procedure

▪ Adding summary lines.

▪ Customizing break lines.

▪ Adding trafficlighting and style attributes.

▪ Using the CALL DEFINE statement.

• Doing More with the REPORT Procedure

▪ Adding summary lines

▪ Customizing break lines.

▪ Adding trafficlighting and style attributes.

▪ Using the CALL DEFINE statement.

• Using ODS Destinations

▪ Introduction to ODS destinations

▪ PRINTER and RTF destinations

▪ CSV and HTML destinations.

▪ Excel and other destinations.

▪ SAS destinations.

• Doing More with ODS

▪ Using ODS options and statements.

▪ Using ODSESCAPECHAR.

▪ Controlling bookmarks and the table of contents.

▪ Using ODS layout

• Introduction to SG Procedures

▪ Introduction to ODS Graphics.

▪ Using the SG procedures.

• Getting Started

▪ Setting up for this course

▪ Data visualization with SAS programming overview.

▪ Layers of a graph.

• SGPLOT Procedure

▪ Introduction

▪ Bar charts.

▪ Dot, line, scatter, and series plots.

▪ Additional basic plots.

▪ Distribution plots.

▪ Fit and confidence plots

• Enhancing Visualization Appearance

▪ Style templates, colors, and fonts.

▪ Group attributes.

▪ Reference lines and text boxes.

▪ Axis appearance.

▪ Legend appearance

▪ User-defined style templates.

▪ Additional statements and examples

• Multi-cell Procedures

▪ Introduction.

▪ SGPANEL procedure

▪ SGSCATTER procedure.

• Additional Procedures

▪ SGPIE procedure.

▪ SGMAP procedure.

• Customizing Visualizations

▪ Introduction to SG annotation.

▪ Drawing space.

▪ LINE and ARROW functions.

▪ TEXT and TEXTCONT functions

▪ SG attribute maps

• More with ODS Graphics

▪ ODS GRAPHICS and destination statements.

▪ ODS layout.

▪ Statistical procedures.

• Graph Template Language

▪ Introduction.

▪ Graph Template Language syntax

▪ Dynamic and macro variables.

▪ Creating Tables

▪ Creating Listings

▪ Output Appearance Options

▪ Common Clinical Trial Graphs

▪ Exporting Data

▪ Define.XML for CDISC SDTM

▪ Define.XML for CDISC ADaM

▪ Programming Considerations

▪ Reviewing Programming Log

▪ Using SAS Programming Procedures for Data Validation

▪ Generate descriptive statistics and explore data with graphs.

▪ Perform analysis of variance and apply multiple comparison techniques

▪ Perform linear regression and assess the assumptions.

▪ Use regression model selection techniques to aid in the choice of predictor variables in multiple regression.

▪ Use diagnostic statistics to assess statistical assumptions and identify potential outliers in multiple regression.

▪ Use chi-square statistics to detect associations among categorical variables.

▪ Fit a multiple logistic regression model.

▪ Score new data using developed models.

This course is mapped to TWO credentials as below:

1. SAS Certified Specialist: Base Programming Using SAS 9.4

2. SAS Certified Professional: Clinical Trials Programming Using SAS 9.4

Participants will be awarded SAS certified digital badges for these on clearing respective SAS exams

Besides participants will also be awarded participation certificate & Learn SAS digital badges for –

1. SAS Programming 1: Essentials

2. SAS Programming 2: Data Manipulation Techniques

3. SAS SQL1: Essentials

4. SAS Macro Language 1: Essentials

5. SAS Report Writing

6. Data Visualization Using SAS ODS Graphics

7. SAS Programming for Clinical Trials

8. Data Analysis and Reporting for Clinical Trials