Clinical SAS Training
Clinical SAS Training
Clinical SAS Training

CLINICAL SAS TRAINING ONLINE

The course curriculum includes theoretical sessions and practical training with real-time oriented project scenarios enabling students to prepare for a career in Clinical SAS. The training program also includes 400 interview questions making students confident in cracking the interviews. Click Here

CLINICAL SAS ONLINE TRAINING

 

Our instructors are skilled in providing you with extensive SAS knowledge and have over 13 years of experience training students in clinical SAS. The training includes real-time oriented project scenarios.

CLINICAL SAS ONLINE TRAINING

The training environment allows students to be engaging and interactive during the sessions. The Clinical SAS training comprises two parts,

The first part comprises the Technical Part, i.e., BASE SAS and Advanced SAS. (Course Time Duration- 30 Hours).
The second part includes the domain part, i.e., CLINICAL SAS. (Course Time Duration- 30 Hours)


Click Here To View Video on Interview Questions

 

Best Clinical SAS Training Online

 

Are you keen to unlock the clinical trial analytics secrets and eager to start your career? If ready, we at BerylSoft invite to our program, which is best clinical SAS training. Here, you can master the art of statistical analysis and open doors to countless opportunities in the world of data science.

 

In today’s data-driven world, SAS (Statistical Analysis System) stands tall as one of the most powerful and sought-after tools for processing, analyzing, and interpreting data.

 

Why Our Online Clinical SAS Training?

 

  1. Expert Guidance: Our team of seasoned SAS professionals brings over thirteen years of industry experience and expertise, ensuring you receive top-notch instruction and guidance throughout the clinical SAS training.
  2. Interactive Learning: Engage in live sessions, practical exercises, and hands-on projects to reinforce your understanding of SAS concepts and real-world applications.
  3. Flexibility and Convenience: Learn from the comfort of your own home or any place of your choice. Our online platform allows you to access course materials and join live classes at your convenience.
  4. Backup Sessions: In case of genuine reasons, if you skip the class, we will make arrangements to help you listen to the missed topics.
  5. Completion Certificate: Upon completion of the training, you’ll earn a certificate, bolstering your resume and showcasing your SAS proficiency to employers.
  6. Comprehensive Curriculum: From SAS programming essentials to advanced data manipulation and visualization techniques, our carefully crafted curriculum covers all aspects to make you SAS proficient.
  7. Dedicated Support: Our support team is always ready to assist you with any queries or concerns during clinical SAS training, ensuring a smooth and enjoyable learning experience.

 

Other Advantages:

 

  • We offer Real-Time Learning Environment. 
  • Assistance will be given to crack the interview.
  • We provide guidance to handle the SAS Certification exam confidently.

Course Details:

  • Course Duration: 4 to 5 Months

 

              1) BASE & ADVANCED SAS TRAINING (Technical Part) – 2Months

              2) CLINICAL SAS TRAINING (Domain Part) – 2Months

 

  • Schedule: Three days per week or weekend classes

 

Ready for the Next Step?

 

If you’re excited about diving into data analytics with SAS, take advantage of this opportunity! Secure your spot by registering for our Clinical SAS Training Online now.

 

To attend a demo session or request more information, reply to this email: info@berylsoft.co.in

                                                                                                              or WhatsApp: 8008957194

Remember, data is the key to making informed decisions, and SAS is your gateway to unlocking that treasure trove of insights. Join us on this enriching learning journey and empower yourself with the skills that drive success in the data-driven world.

Let’s embark on this transformative journey together!

 

BASE SAS TRAINING SYLLABUS

 

1) SAS DATASET CREATION

 

1)  INPUT METHOD

2)  ASSIGNMENT METHOD

3)  SET METHOD

4)  LIBNAME METHOD

5)  INFILE METHOD

6)  FILENAME METHOD

7)  %INCLUDE STATEMENT

8)  PROC IMPORT

9)  ARRAY METHOD

10) DO, WHILE LOOPS

11) PROC SQL METHOD

12) SQL PASS-THROUGH facility – DATA fetching from SERVERS

13) SAS/ACCESS                – DATA fetching from SERVERS

2) DATASTEP STATEMENTS

 

1) DATA
2) CARDS
3) DATALINES
4) INPUT STATEMENT: 1) LIST 2) NAMED 3) FORMATTED METHODS
5) INFORMAT
6) FORMAT
7) INFILE STATEMENT
8) FILE
9) INPUT
10) PUT
11) LABEL
12) LENGTH
13) ARRAY
14) ATTRIB
15) WHERE
16) OUTPUT
17) IF – THEN
18) IF-THEN-ELSE IF
19) KEEP
20) DROP
21) RENAME
22) RETAIN
23) ASSIGNMENT
24) ABORT
25) ABEND
26) ERROR
27) BY
28) DELETE
29) DO -ITERATE
30) DO – WHILE
31) DO – END
32) DO – UNTIL
33) SET
34) MERGE
35) UPDATE
36) SUM
37) WHEN
38) SELECT
39) REMOVE
40) RETAIN

3) DATASTEP FUNCTIONS

 

1) CHARACTER FUNCTIONS
2) MISSING FUNCTIONS
3) DATE FUNCTIONS
4) CALL ROUTINES
5) NUMERICAL FUNCTIONS
6) MATHEMATICAL FUNCTIONS
7) SAS BITWISE FUNCTIONS

4) SAS DATASET OPTIONS

 

1) keep=
2) drop=
3) Rename=
4) in=
5) where=
6) Firstobs=
7) Obs=

5) SAS SYSTEM OPTIONS

 

1) CENTER or NOCENTER. CENTER
2) DATE or NODATE. DATE
3) NUMBER or NO NUMBER.
4) LINESIZE = n
5) PAGESIZE = n,
6) PAGENO = n …….etc.,

6) AUTOMATIC VARIABLES

 

1. _ERROR_
2. _N_
3. _CHARACTER_
4. _NUMERIC_
4. -CHARACTER-
5. -NUMERIC-
6. _AUTOMATIC_
7. _ALL_

7) SAS PROCEDURES

 

1. DATA HANDLING PROCEDURES: PROC COPY, PROC CONTENTS, PROC SORT, PROC PRINT, PROC DELETE, PROC OPTIONS, PROC DATASETS, PROC IMPORT, PROC EXPORT, PROC APPEND, PROC SETINIT, PROC RANK
2. STATISTICAL PROCEDURES: PROC MEANS, PROC FREQUENCY, PROC SUMMARY, PROC UNIVARIATE
3. REPORTING PROCEDURES: PROC FORMAT, PROC TRANSPOSE, PROC TABULATE, PROC REPORT, PROC PLOT, PROC GCHART
4. EVALUATION PROCEDURES: PROC COMPARE

8) SAS/ODS

 

1) Creating RTF File
2) Creating HTML File
3) Creating PDF File

9) SAS/GRAPH

 

1) PROC PLOT
2) PROC GCHART
3) Vertical, Horizontal, Pie
4) Group, Subgroups
5) Plot Procedure
6) Multiple Plots & Overlay
7) Symbol Statement
8) Title And Footnote Statements

10) SAS/STAT

1) PROC UNIVARIATE
2) PROC CORRELATION
3) PROC ANOVA
4) PROC REG

Advanced SAS Training Syllabus

 

11) SAS SQL

 

1) Retrieving Data from single Tables
2) Retrieving Data from Multiple Tables
3) Adavntage of DICTIONARY.TABLES
4) SAS INTEGRITY CONSTRAINTS
5) SAS INDEXES
6) SAS VIEWS
7) How to create Integrity constraints, Indexes, and views using both SAS Datasteo & SQL method

1. Retrieving Data from single Tables:
****************************************
1) What Is the SQL Procedure?
2) What are the tasks followed by PROC SQL that enable you
3) Creating and Updating Tables
4) Creating Tables from Column Definitions
5) Creating Tables from a Query Result
6) Creating Tables like an Existing Table
7) Copying an Existing Table
8) Using Data Set Options
9) Updating Data Values in a Table
10) Updating All Rows in a Column with the same Expression
11) Updating Rows in a Column with the Different Expression
12) Handling Update Errors
13) what is CASE OPERAND
14) what is the CASE STATEMENT
15) What is the difference between
UPDATE & CASE OPERAND statements
16) Overview of the SELECT Statement
17) Selecting Columns in a Table
18) Creating New Columns
19) Sorting Data
20) Retrieving Rows That Satisfy a Condition
21) Summarizing Data
22) Grouping Data
23) Filtering Grouped Data
24) Validating a Query
25) How will you Filter the Data using the WHERE clause?
26) What is a WHERE condition?
27) What is a Having Condition?
28) What is the difference between WHERE & Having?
29) Creating New Columns
30) Adding Text to Output
31) Calculating Values
32) Assigning a Column Alias
33) Referring to a Calculated Column by Alias
34) Assigning Values Conditionally
35) Using a Simple CASE Expression
36) Using the CASE-OPERAND Form
37) Replacing Missing Values
38) Specifying Column Attributes
39) Checking the Data types & Attributes in LOG
40) Difference between DROP, DELETE statements
41) ALTERING COLUMNS
42) Adding a column
43) Deleting a column
44) Modifying a column
45) SQL Functions: coalesce, Monotonic, N, NMISS, MISSING, DISTINCT, UNIQUE, UPPER, LOWER, SUBSTRING,PUT, INPUT,SUMMARY functions…etc
46) Which is more faster- Data Step / Proc SQL?

 

/****************

2) Retrieving Data from Multiple Tables

47) Selecting Data from More Than One Table By Using Joins
48) Combining Queries with SET Operators: UNION, OUTER UNION, OUTER UNION CORR, INTERSECT, EXCEPT
49) Using Sub queries to Select Data
50) When to Use JOINS and Sub queries
51) Combining Queries with JOIN Operators: FULL JOIN, INNER JOIN, LEFT JOIN, RIGHT JOIN
52) What is the difference between MERGE & JOIN, output explanation with comparison
52) What is the difference between SET & UNION, output explanation with comparison
53) Generate reports
54) Generate summary statistics
55) Retrieve & Manipulate data from tables or views
56) Combine data from tables or views
57) Create tables, views, Integrity Constraints, and indexes
58) Update and retrieve data from database management system (DBMS) tables

12) SAS MACROS

 

1) Macro Concepts
2) Macros And Macro Variables
3) Creating Macro Variables
4) Using Macro Variables
5) Creating Modular Code With Macros
6) Invoking A Macro
8) Adding Parameters To Macros
9) Macros With Conditional Logic
10) Using Various Procedures In Macros
11) Automatic Variables
12) Macro Functions
13) Including External Macros
14) Macro Debugging Functions

13) Real-time Project Oriented Environment

 

14) Architecture of Project explanation

15) Architecture of SAS Technology explanation

16) How to write efficient SAS Programming?

CLINICAL SAS TRAINING SYLLABUS

 

1. Introduction and Flow of Clinical Trials using SAS Technology

 

1. Introduction on Clinical Trials

***********************************
1) What is a Clinical trial?
2) Biopharmaceutical Research & Development Process
3) How do Clinical Trials work from TRIAL to TREATMENT?
4) Why are clinical trials conducted?
5) What is the Role of SAS in Clinical Research ?
6) What is the Role of SAS Programmer in Clinical Research ?
7) How Drug Development Process will ake ?
8) What are different Clinical Trial Phases ?Their Responsibilities?
9) Indudtry Regulations & Standards :CDISC, ICH
10) What are CDISC standards ?
11) What are Data Foundational standards?
12) What are PROTOCOL & SAP (Statistical Analysis Plan)
13) What are the Contents of a Clinical Trial Protocol
14) What are the Contents of a Clinical Trial SAP
15) Explain the Clinical Trial Process ?
16) Define Clinical Domain Terms : Study design, ARM, ELEMENT, EPOCH, RANDOMIZATION, BLINDING, BIAS, PARALLEL, CROSSOVER DESIGN, PLACEBO, CONTROL GROUP, TREATMENT GROUP
17) CLINICAL TRIAL DATA MANAGEMENT
18) what is FDA submission
19) Flow of FDA submission

 

2. Describe the structure and purpose of the CDISC SDTM data model.

1. Variables and Observations

2. Datasets and Domains

3. General Observation Classes

4. SDTM standard Domain models

5. General Assumptions for All Domains

6. MODELS FOR SPECIAL-PURPOSE DOMAINS

 

  • COMMENTS
  • DEMOGRAPHICS
  • SUBJECT ELEMENTS
  • SUBJECT VISITS

7) DOMAIN MODELS BASED ON THE GENERAL OBSERVATION CLASSES

 

  • INTERVENTIONS
  • Concomitant and Prior Medications
  • Exposure Domains
  • Exposure
  • Exposure as Collected
  • Procedure
  • Substance Use
  • EVENTS
  • Adverse Events (AE)
  • Clinical Events (CE)
  • Disposition (DS)
  • Protocol Deviations(DV)
  • Medical History(MH)
  • FINDINGS
  • Drug Accountability(DA)
  • Death Details(DD)
  • ECG Results(EG)
  • Inclusion/Exclusion Criteria Not Met (IE)
  • Laboratory Test Results (LB)
  • Microbiology Domains (MB and MS)
  • Microbiology Specimen(MB)
  • Microbiology Susceptibility (MS)
  • Pharmacokinetics Domains(PC and PP)
  • Pharmacokinetics Concentrations (PC)
  • Pharmacokinetics Parameters (PP)
  • Physical Examination(PE)
  • Questionnaires(QS)
  • Subject Characteristics (SC)
  • Oncology Domains: ( TU, TR, RS)
  • Tumor Identification(TU)
  • Tumor Response (TR)
  • Disease Response(RS)
  • Vital Signs(VS)
  • FINDINGS ABOUT EVENTS OR INTERVENTIONS
  • When to Use Findings About
  • Naming Findings About Domains
  • Variables Unique to Findings About
  • Findings About
  • Skin Response (SR)

 8) TRIAL DESIGN DATASETS 

 

  • Purpose of Trial Design Model
  • Definitions of Trial Design Concepts
  • Current and Future Contents of the Trial Design Model
  • EXPERIMENTAL DESIGN: TA AND TE
  • Trial Arms
  • Trial Elements
  • SCHEDULING OF ASSESSMENTS: TV AND TD
  • Trial Visits (TV)
  • Trial Disease Assessments (TD)
  • TRIAL SUMMARY AND ELIGIBILITY: TI AND TS
  • Trial inclusion/Exclusion Criteria (TI)
  • Trial Summary information(TS)

  9)HOW TO MODEL THE DESIGN OF A CLINICAL TRIAL 

 

 10) REPRESENTING RELATIONSHIPS AND DATA

 

  • RELATING GROUPS OF RECORDS WITHIN A DOMAIN USING THE –GRPID VARIABLE
  • –GRPID Example
  • RELATING PEER RECORDS
  • RELREC Dataset
  • RELREC Dataset Examples
  • RELATING DATASETS
  • RELREC Dataset Relationship Example
  • RELATING NON-STANDARD VARIABLES VALUES TO A PARENT DOMAIN

 11) Supplemental Qualifiers: SUPP– Datasets 

 

  • Submitting Supplemental Qualifiers in Separate Datasets
  • SUPP– Examples
  • When Not to Use Supplemental Qualifiers

 12) RELATING COMMENTS TO A PARENT DOMAIN 

 

  •     Guidelines For Differentiating Between Events, Findings, and Findings About Events …

3. Describe the structure and purpose of the CDISC ADaM data model.

1) what is ADAM, and definition

2) Analysis Datasets and ADaM Datasets

3) Fundamentals of the ADaM Standard

4) ADaM Subject-Level Analysis Dataset (ADSL)

5) ADaM Basic Data Structure (BDS)

6) Standard ADaM Variables & ADaM Variable Conventions

7) ADSL Variables

8) BDS Variables

Identifier Variables for BDS Datasets Record-Level Treatment and Dose Variables for BDS Datasets

Timing Variables for BDS Datasets Analysis Parameter Variables for BDS Datasets

Analysis Descriptor Variables for BDS Datasets

Time-to-Event Variables for BDS Datasets Toxicity and Range Variables for BDS Datasets

Indicator Variables for BDS Datasets

Datapoint Traceability Variables

9) ANALYSIS-ENABLING VARIABLES DIFFERENCES BETWEEN SDTMAND ADAMPOPULATION AND BASELINE FLAGS

10) CREATION OF DERIVED COLUMNS VERSUS CREATION OF DERIVED ROWS

11) IDENTIFICATION OF ROWS USED FOR ANALYSIS

Identification of Rows Used in a Timepoint Imputation Analysis

12) Subject-Level Analysis Data

13) Dates and Date imputations

14) Treatment emergence, pre, and post-treatment occurrences

15) ADaM BDS Derived Parameters Creation

16) ADaM BDS – Derived records creation

17) ADaM BDS – Baseline Concepts

18) ADaM BDS – Visit Windowing

19) Record-level treatment variables

20) ADaM BDS – Analysis Flags

21) ADaM BDS – Data Imputation

22) ADaM BDS – Criteria Flags

23) ADaM BDS – Shift variables

24) Time to event-related variables

25) OCCDS-specific variables

4. Describe the structure and purpose of Tables, Listing & Figures

5. Describe the contents and purpose of define.xml

6. Submission of CDISC packages