Clinical SAS TRAINING IN CHENNAI

SAS Training Institute in Chennai provides BEST Clinical SAS training in chennai strongly determined to provide certification and Placement training on SAS Clinical Data Integration. It provides the foundation you need to define analysis data sets and support strategic analyses such as cross-study and advanced safety analysis. With SAS, you can gain both speed and efficiency by automating repeatable clinical trial data integration tasks.

Clinical SAS Programming Training Course Content

Base SAS Training

SAS Programming I: Essentials

This foundation course focuses on the following key areas: reading raw data files and SAS data sets; investigating and summarizing data by generating frequency tables and descriptive statistics; creating SAS variables and recoding data values; sub setting data; combining multiple SAS files; creating listing, summary, HTML, and graph reports.

SAS Programming II: Data Manipulation Techniques

This intermediate course focuses on how to manage SAS data set input and output, work with different data types, and manipulate data. Specifically, the course discusses using the DATA step to control SAS data set input and output, combine SAS data sets, summarize data, process data iteratively with DO loops and arrays, and perform data manipulations and transformations.

Advance SAS Training

SAS Macro Language I: Essential

This course focuses on the components of the SAS macro facility and how to design, write, and debug macro systems. Emphasis is placed on understanding how programs with macro code are processed.

SQL Processing with SAS

This intermediate course focuses on using SQL as a data query and manipulation tool. You learn to use the SQL procedure as a data retrieval tool within SAS programs. Specifically, you learn how to perform queries on data; retrieve data from multiple tables; create views, indexes, and tables; and update or delete values in existing tables and views. Using features of the SQL procedure to debug, test, and optimize the performance of SQL queries is also discussed.

SAS Programming 3: Advanced Techniques and Efficiencies

This course is for SAS programmers who prepare data for analysis. The comparisons of manipulation techniques and resource cost benefits are designed to help programmers choose the most appropriate technique for their data situation.

SAS Report & Graph

SAS Report Writing 1: Essentials

This course teaches you how to create detail and summary tabular reports using Base SAS procedures. You also learn how to enhance your reports using the Output Delivery System (ODS).

SAS Color Graphics

Control the appearance of text in reports, create two-dimensional displays of data, create bar charts and pie charts, create geographic representations of data, and produce clickable and printable graphic reports.

For Statistics Students

Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression (3 Days) + Introductory Class of Basic Statistics

This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression. This course (or equivalent knowledge) is a prerequisite to many of the courses in the statistical analysis curriculum.

For Non Statistic Student

SAS Clinical Data Integration(CDI) + Basic Introduction to CDISC SDTM Standards

This course introduces the essentials for using SAS Data Integration Studio to register source and target tables and to create and manage jobs using transformations to read and manipulate data. Using the clinical features, this course shows how to import CDISC standards (or other standards) for domain structure and contents into the metadata, build clinical domain target table metadata from those standards, create jobs to load clinical domains, to validate the structure and content of the clinical domains based on the standards, and to generate CDISC standard define.xml files describing the domain tables for clinical submissions.

Practical Project work shop

• Clinical Research Theory
• Introduction to clinical research
• Detail about data generation in clinical research
• Familiarize with frequently used Clinical Trial terminology and GCP (Good Clinical Practices)
• Overview of LOCF, BOCF & Change from Baseline
• Clinical Research Project
• Creation of Analysis dataset specification
• Generation of Analysis dataset
• Generation of following tables for a given studies (Any One)
• Adverse event (AE)
• Demographic (DM)
• Medical History (MH)
• Lab data (LB)

Why Biostatistics and SAS Programming?

A plethora of technologies and the ever-increasing complexity, volume, regulatory requirements, and geographic spread of clinical trials is forcing companies to examine their core focus areas, while encouraging them to outsource clinical data management, SAS programming and biostatistics to a trusted partner.

With the primary focus of a biopharmaceutical company or a CRO being science and research, they need a versatile technology organization with clinical research expertise to take care of all their data management, clinical programming and IT needs without incurring additional fixed costs. They also look for supporting partners who need to be responsive and can ramp up and down to meet the peaks and valleys of work load, at a reasonable cost while ensuring high quality working in regulated environments.

Clinical SAS Training and Services

• Standardized approach for analysis files
• Transportable tools to support standard non-efficacy reports
• Programming based on analysis data
• Independent quality reviews
• Customized clinical data outputs
• Proactive, results-oriented approach to programming
• Programming for integrated summary of safety and efficacy
• Adoption of sponsor’s programming (SOP) Preparation of Case Report Tabulations (CRT) for submission
• Programming based on data validation plan
• Programming of tables/listings/figures for clinical study report and data monitoring committee

• Consultation on study design
• Protocol development
• Randomization
• Statistical Analysis Plan (SAP)
• Interim analysis
• DSMB participation
• Sample size calculations