Clinical SAS Analytics

Work with the latest industry tools and learn how to apply them in the domain of Clinical SAS Analytics.

Overview

Why Clinical SAS Analytics

With the increasing demand for medicines, there is a building pressure of pharmaceutical research and development and using computational tools is automating many of these processes and making them more efficient. Clinical Research is one such growing industry that require trained specialist in Bio-IT.

In This Course

Learn data migration and manipulation using SAS. Get trained in advanced concepts of SAS such as set operators, statistical and analytical procedures. Also learn how the Clinical Trial Management process works and the regulatory activities involved in clinical trials in humans.

The Sollers Advantage

Our Clinical SAS Analytics program is taught by faculty experienced in the industry. The program includes a comprehensive 3-month academic internship where students work on three distinct real time projects using SAS analytics.

Learning Outcomes

  • Create basic detail and summary reports using SAS procedures.
  • Identify and correct data, syntax and programming logic errors.
  • Create temporary and permanent SAS data sets, investigate SAS data.
  • Libraries using base SAS utility procedures and access an Excel workbook.
  • Create and manipulate SAS date values, export data to create standard and comma delimited raw data files.
  • Control which observations and variables in a SAS data set are processed and output.
  • Sort observations in a SAS data set conditionally execute SAS statements.
  • Use assignment statements in the DATA step, modify variable attributes using options and statements in the DATA step.
  • Use SAS functions to manipulate character data, numeric data, and SAS date values. Use SAS functions to convert character data to numeric and vice versa.
  • Process data using DO LOOPS, validate data, clean data, generate list reports using the PRINT procedure.
  • Generate summary reports and frequency tables using base SAS procedures.
  • Enhance reports using user-defined formats, titles, footnotes and SAS System reporting.
  • Identify and resolve programming logic errors, recognize and correct syntax errors, examine and resolve data errors.
  • Use SAS procedures to obtain descriptive statistics for clinical trials data.
  • Use PROC FREQ to obtain p-values for categorical data.
  • Create output data sets from statistical procedures.
  • Use PROC REPORT to produce tables and listings for clinical trials reports.
  • Use ODS and global statements to produce and augment clinical trials reports.
  • Identify key CDISC principals and terms.
  • Describe the structure and purpose of the CDISC, SDTM and ADAM data model.

Syllabus

Introduction to SAS/BASE

  • SAS Installation Process
  • Windows and Commands in the SAS Windowing Environment

SAS Base

  • SAS Data Sets (Temp and Perm Datasets)
  • Submitting a Program in the SAS Windowing Environment
  • Reading the SAS Log
  • Viewing Your Results in the Output Window
  • SAS Data Libraries
  • Viewing Data Sets with SAS Explorer
  • Using SAS System Options

Migration of Data

  • PROC IMPORT Procedure
  • PROC EXPORT Procedure
  • Principles of Exporting Data to FDA and Non-FDA Organizations

Manipulations of Data

  • PROC COPY Procedure
  • PROC APPEND Procedure
  • PROC SORT Procedure

Manipulations of Data

  • PROC SQL Procedure
  •  PROC TRANSPOSE Procedure

Using Functions

  • Working with Numeric Functions
  • Working with Character Functions
  • Working with Date/Time and Stat Functions
  • Using LOCF Techniques to Clinical Trials Data

 Statistical Procedures

  • PROC UNIVARIATE Procedure
  • PROC CORR Procedure
  • PROC REG Procedure

Using SAS/ODS/GRAPH

  • For Exploring data (PROC CHART – vbar, hbar, pie, block,)
  • PROC PLOT (plot, boxplot) For professional charts (FDA) (PROC GCHART-pattern, PROC GPLOTsymbol)

Reporting of Clinical Trial Data

  • For Ad-hoc (PROC PRINT)
  • Introduction to GraphsFor Generating Tablesand Listings (PROC REPORT)
  • For SUMMARY Tables (PROC TABULATE)

Using SAS/ Macro

  • Introduction to SAS/Macros
  • Create and use user-defined and automatic macro variables

Using SAS/ Macro facility- Part II

  • Using System Options to Display Values of Macro Variables in the SAS Log
    (MPRINT, SYMBOLGEN, MLOGIC, MACROGEN)
  • Using SAS/Macros Procedures

Validation of Clinical Trial Data

  • The Principles of Validation Programming
  • Utilizing the Log File to Validate Clinical Trial Datasets
  • Using SAS Procedures to Verify Clinical Trial Reporting (PROC COMPARE, FREQ, UNIVARIATE)
  • Identifying and Resolving Data and
    Syntax Problems.

Using Edit Checks, CDISC/SDTM, and ADaM with Demograpihc data

  • Working with Edit Check Specifications
  • Identify the Classes and Modules of Clinical Trial Data
  •  Understanding of CDISC Principles and Terms

 Using Edit Checks, CDISC/SDTM, and ADaM with Adverse events Data

  • Working with Edit Check Specifications
  • Identify the Classes and Modules of Clinical Trial Data
  • Understanding of CDISC Principles and Terms

Using CDISC/SDTM and ADaM

  • Reporting clinical data

Clinical Research

  • Basic of research
  • Phases of clinical research
  • ICH-GCP guidelines
  • HIPPA and IRB
  • Data flow in clinical research

Clinical Data Management (Self Paced)

  • Data Management oracle clinical
  • Data extraction from clinical research

Project 1: Analysis Dataset Development
Project 2: QC of SDTM Dataset Development
Project 3: QC of AE and DM Tables

INTERNSHIP

1st Project

Analysis Dataset Development

Generate Laboratory analysis dataset(ADLB) by using raw lab data (LB).

2nd Project

QC of SDTM Dataset Development

Generate DM (SDTM) dataset as perCDISC standard by using raw DM &Com var dataset. Validate it with primary demographic dataset by writing independent code in SAS.

3rd Project

QC of AE and DM Tables

  •  Generate the demography report by writing independent code in SAS using analysis dataset for demography.
  •  Generate the adverse event summary report classified by MedDRA system organ class by writing independent code in SAS using analysis datasets for demography and adverse event.

Course Duration

Engagement

130 Hours

For information regarding fee and/or reserving your spot, contact our Admissions Team.

OUR STUDENTS WORK AT

Sollers partners with industry-leading corporations and provides them with ready-on-day-one employees. We record an 82% placement rate within three months of graduation.

Financial Options

Sollers has devised viable financial options for you to ensure tuition does not get in the way of your education. Now, you can focus your attention where it needs to be – in the classroom!

Career Guidance

After the completion of program, we assist our students with interview coaching, resume building sessions, conduct mock interviews, job readiness training and make them competent to venture into the corporate world.

Student Testimonials

  • Emmanet T.
    The Clinical Trial Management course is extensive, and the professors are thorough in delivering the course materials using real life experience and insight. Career Services helped me throughout the interview process, and I landed my dream job with the very first interview after graduating from Sollers.
    Emmanet T.
  • Rudolf M.
    Thanks to the academic team at Sollers, I was able to gain quick admission with financial options to pay tuition. Career Services was very helpful with resume and interview prep. They sent me job postings with major hospitals and pharma companies which helped me land my current job as Data Manager in cancer clinical trials.
    Rudolf M.

Campus Visit

FAQs

Clinical SAS Analytics is one of the fastest growing career paths and best paid career paths. Clinical SAS analysts use a combination of programming, statistical perspective and domain expertise to do so. They are expected to be in large demand as businesses grow in storing the data that they generate to achieve competitive differentiation in their products and services.

The program focuses on the development of dynamic emerging Clinical SAS analysts into the healthcare industry for analytics. The curriculum will focus on Clinical research, clinical data management, SAS, SQL, statistical modeling, data visualization with Tableau and business analytics.

Our students come from all different backgrounds, with the most competitive candidates including those with Bachelors, Masters, or PhD Degree in Statistics, and Sciences. Some have similar backgrounds to Clinical Research.

Our 180-hour program can be completed in 9-12 weeks- All of the programs offered by Sollers have been designed to best suit those seeking to enter the field of clinical research, health, IT among other domains.

It is preferable that a student be a Bachelors or a Masters graduate in Science including Statistics, Engineering, Computer Science or other sciences. Some students may have sufficient knowledge in some areas.

Clinical SAS Analytics is one of the fastest growing and best paid career paths. Indeed.com reported the median salary for Clinical SAS Programmers at $102,000 for 2016. Medgadget reports that the market for clinical data analytics is expected to grow to $12 Billion by the end of 2022, in their new market research report.

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Anybody with a minimum undergraduate degree from science or related backgrounds who would like to establish or advance their career.