SAS Clinical Trial Data Analytics & Statistical Modeling
- 40 weeks
- 240 hours
Learn best practices for data management, analysis, and reporting. Ideal for roles in pharmaceutical, biotech, and healthcare industries.
Course Overview
SAS Clinical Trial Data Analytics & Statistical Modeling is a comprehensive course designed to equip participants with the essential skills and knowledge required for effectively analyzing clinical trial data using SAS software. This course covers fundamental statistical concepts, data management techniques, and advanced analytics methods tailored specifically for clinical research. Participants will learn to navigate through complex datasets, perform statistical analysis, and interpret results accurately to support evidence-based decision-making in clinical trials. Through hands-on exercises and real-world case studies, attendees will gain practical experience in applying SAS tools to address challenges commonly encountered in clinical research settings. By the end of the course, participants will be proficient in leveraging SAS for data analytics and statistical modeling tasks crucial for successful clinical trial outcomes.
Advantage
This comprehensive course provides extensive training in SAS for clinical trial data analytics and statistical modeling, covering Base SAS and Advanced SAS for Global Certification, as well as specific modules on ADaM, TLF, and SDTM. Students will learn essential concepts, techniques, and best practices for clinical trial data management, analysis, and reporting using SAS software. The curriculum is aligned with industry standards and designed to prepare students for roles in pharmaceutical, biotech, and healthcare industries involved in clinical research and development.
What you'll learn
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Base and Advanced SAS for clinical trial data analytics
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Specialized modules: ADaM, TLF, SDTM
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Clinical trial data management best practices.
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Analysis and reporting using SAS software
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Ideal for pharmaceutical, biotech, healthcare roles
- Components of the SAS System
- Data-Driven Tasks
- Turning data into Information
- Introducing to SAS Programs
- Running SAS Programs
- Mastering Fundamental Concepts
- Diagnosing and Correcting Syntax Errors
- SAS Data Libraries
- Getting Started with the PRINT Procedure
- Sequencing and Grouping Observations
- Identifying Observations
- Special WHERE Statement Operators
- Customizing Report Appearance
- Formatting Data Values
- Creating HTML Reports
- Reading Raw Data Files: Column Input
- Reading Raw Data Files: Formatted Input
- Examining Data Errors
- Assigning Variable Attributes
- Changing Variable Attributes
- Reading Excel Spreadsheets
- Reading SAS Data Sets and Creating Variables
- Conditional Processing
- Dropping and Keeping Variables
- Reading Excel Spreadsheets Containing Date Fields
- Concatenating SAS Data Sets
- Merging SAS Data Sets
- Combining SAS Data Sets: Additional Features
- Introduction to Summary Reports
- Basic Summary Reports
- The Report Procedure
- The Tabulate Procedure
- Producing Bar and pie Chart
- Enhancing output
- Producing Plots
- Overview
- Review of SAS basics
- Review of DATA Step Processing
- Review of Displaying SAS Data Sets
- Working with Existing SAS Data Sets
- Outputting Multiple Observations
- Writing to Multiple SAS Data Sets
- Selecting Variables and Observations
- Writing to an External File
- Creating an Accumulating Total variable
- Accumulating Totals for a Group of Data
- Reading Delimited Raw Data Files
- Controlling When a Record Loads
- Reading Hierarchical Raw data Files
- Introduction
- Manipulating Character values
- Manipulating Numeric values
- Manipulating Numeric values based on Dates
- Converting variable Type
- Using the PUT Statement
- Using the DEBUG Option
- Do Loop Processing
- SAS Array Processing
- Using SAS Arrays
- Match-merging Two or more SAS Data Sets
- Simple Joins Using the SQL Procedure
- Setting Up for the Course
- What Is SQL?
- Introduction to the SQL Procedure
- Demonstration: Exploring the customer Table
- Generating Simple Reports
- Summarizing and Grouping Data
- Creating and Managing Tables
- Using DICTIONARY Tables
- Introduction to SQL Joins
- Inner Joins
- Outer Joins
- Complex Joins
- Performing a Reflexive Join
- Subquery in WHERE and HAVING clauses
- In-Line Views (Query in the FROM Clause)
- Subquery in the SELECT Clause
- Introduction to Set Operators
- INTERSECT, EXCEPT, and UNION
- OUTER UNION
- Creating User-Defined Macro Variables
- Creating Data-Driven Macro Variables with PROC SQL
- Overview of SAS/ACCESS Technology
- SQL Pass-Through Facility
- Demonstration: Using an SQL Pass-Through Query
- SAS/ACCESS LIBNAME Statement
- Demonstration: Using the SAS/ACCESS LIBNAME Statement
- FEDSQL Procedure
- Why SAS Macro?
- Setting Up for This Course
- Solutions
- Program Flow
- Creating and Using Macro Variables
- Solutions
- Macro Functions
- Using SQL to Create Macro Variables
- Using the DATA Step to Create Macro Variables
- Indirect References to Macro Variables
- Solutions
- Defining and Calling a Macro
- Macro Variable Scope
- Conditional Processing
- Iterative Processing
- Solutions
- Storing Macros
- Generating Data-Dependent Code
- Validating Parameters and Documenting Macros
- Solutions
- Descriptive statistics
- Inferential statistics
- Examining data distributions
- Obtaining and interpreting sample statistics using the UNIVARIATE procedure
- Examining data distributions graphically in the UNIVARIATE and FREQ procedures
- Constructing confidence intervals
- Performing simple tests of hypothesis
- Performing tests of differences between two group means using PROC TTEST
- Performing one-way ANOVA with the GLM procedure
- Performing post-hoc multiple comparisons tests in PROC GLM
- Producing correlations with the CORR procedure
- Fitting a simple linear regression model with the REG procedure
- Performing two-way ANOVA with and without interactions
- Understanding the concepts of multiple regression
- Automated model selection techniques in PROC GLMSELECT to choose from among several candidate models
- Interpreting and comparison of selected models
- Examining residuals
- Investigating influential observations
- Assessing collinearity
- Understanding the concepts of predictive modelling
- Understanding the importance of data partitioning
- Understanding the concepts of scoring
- Obtaining predictions (scoring) for new data using PROC GLMSELECT and PROC PLM
- Producing frequency tables with the FREQ procedure
- Examining tests for general and linear association using the FREQ procedure
- Understanding exact tests
- Understanding the concepts of logistic regression
- Fitting univariate and multivariate logistic regression models using the LOGISTIC procedure
- Using automated model selection techniques in PROC LOGISTIC including interaction terms
- Obtaining predictions (scoring) for new data using PROC PLM
- Overview of drug development phases
- Roles and responsibilities in drug development
- Importance of data management in drug development
- Regulatory authorities and their roles
- New drug application (NDA) process
- Generic drug approval process
- Biologics license application (BLA) process
- Phases of clinical trials
- Study design and protocol development
- Patient recruitment and enrollment
- Data collection and management in clinical trials
- Purpose and benefits of CDISC standards
- Overview of CDISC models: SDTM, ADaM, ODM
- Importance of data standards in clinical research
- What is SDTM?
- Observations and variables in SDTM
- Special purpose datasets
- General observation classes in SDTM
- Introduction to SDTM domain models
- Special purpose domains: DM, CO, SE, SV
- Interventions: CM, EX, SU, EC
- Events: AE, DS, MH, DV, HO
- Findings: LB, EG, VS, PE, IE, DD, QS
- Trial design domains: TA, TE, TS, TI, TV
- Supplemental qualifiers domains and RELREC
- SDTM mapping programming using SAS
- Real-time project on SDTM
- SDTM annotation on CRF
- Purpose and significance of ADaM
- Key concepts in ADaM
- ADaM naming conventions
- ADaM implementation process
- Fundamentals of ADaM standards
- Variables in general
- ADSL variables
- BDS variables
- Real-time project on ADaM
- ADSL domain
- ADAE domain
- ADVS domain
- Generating summary reports
- Introduction to ICH E6, E9, and E3 guidelines
- Protocol and CRF/eCRF
- Statistical analysis plan (SAP)
- Mock shells
- Introduction to the clinical study report
- SAS program development and validation (QC)
- Generating listings and graphs
Admission Process
Please call to admission counselor for course fees, registration fees, EMI fecilities,registration form and other formalities. Contact to admission counselor
+91-9830247087
Who Can Join?
Any graduate with knowledge of basic computing.
Requirment
1. Personal computer/laptop with webcam and microphone
2. Stable internet connections
Payment Details
Bank Details:
KLMS HANDS-ON SYSTEMS PRIVET LIMITED
Account Number: 19700200000420
IFSC Code: BARB0SALTLA (5th letter is numeric zero)
UPI Payment: 9432257052@okbizaxis
- Live Instructor-Led Course
- Project and Case Studies
- Certificate of completion
- Learn from Experts
- Placement Assistance
- Assistance for Global Certification
