Data management plays a vital role in ensuring the accuracy and reliability of clinical trial data. Clinical trial companies globally use SAS for clinical analysis and submission. So, master the powerful SAS programming principles with this clinical data management course. This Clinical Data Manager course will enhance your understanding of SAS programming. It will help you prepare for clinical data management jobs using SAS.
The course will educate you about the reliable components of the pharmaceutical industry. It will then take you through the clinical trial life cycle and the types of data collected in clinical trials. Again, it will introduce you to the study documents needed for clinical trials. For instance, you’ll learn about clinical study protocols and ethical consent. The course will also cover the inclusion-exclusion criteria and statistical analysis plans.
The clinical data management training also covers the essentials of an excellent clinical study report. First, it will introduce you to the general SAS programming steps. Next, you’ll learn how to program demographics tables and import raw demographic data into the SAS. You’ll understand how to derive age, sex and race variables and get summary statistics for each variable. Again, you’ll learn how to stack all the statistics together. The course will also explain how to fix precision points. You’ll learn how to transpose data and correct the order of statistical parameters. Then, you’ll understand how to create the final report and add the finishing touches.
So, enrol now to perform excellent clinical data management services.
Learning outcome
Understand the fundamentals and frameworks of Clinical Data.
Develop proficiency in SAS programming for data analysis.
Master the techniques for data validation and quality control.
Learn data setup, maintenance, and compliance protocols.
Acquire skills in managing clinical trials data effectively.
Essential for mastering Clinical Data Management using industry-standard SAS tools.
Improves your data handling skills specific to clinical trials.
Bridges the gap between theoretical knowledge and practical application.
Enhances career prospects in the booming healthcare data sector.
Provides certification that is recognized in the healthcare industry.
Career Path
Clinical Data Manager
SAS Programmer
Clinical Data Coordinator
Clinical Data Analyst
Data Quality Manager
Requirements
Basic understanding of data analysis.
Familiarity with clinical trial processes.
Eagerness to develop technical skills in SAS programming.
Course Curriculum
Course Curriculum
Course Promo
Course Promo
00:01:00
Unit 01: Introduction
1.1 Components of the Pharma Industry
00:05:00
1.2 Phases of Clinical Trials
00:06:00
1.3 Data and Reports in Clinical Trials
00:04:00
1.4 Types of Data
00:05:00
Unit 02: Knowledge on Clinical Study Documents
2.1 Clinical Study Protocol
00:02:00
2.2 Ethical Consent
00:01:00
2.3 Inclusion-Exclusion Criteria
00:01:00
2.4 Statistical Analysis Plan: SAP, Mockshell and CRF
00:04:00
Unit 03: Developing the Clinical Study Reports
3.1 General SAS Programming Steps
00:02:00
3.2 One Search Report: Demographics Table
00:04:00
3.3 Understanding the Demographics Table
00:03:00
3.4 Programming the Demographics Table
00:05:00
3.5 Importing Raw Demographic Data into the SAS
00:04:00
3.6 Deciding what Procedure to Use
00:02:00
3.7 Deriving the AGE variable
00:10:00
3.8 Obtaining Summary Statistics for AGE
00:04:00
3.9 Adding the 3rd Treatment Group using Explicit Output
00:05:00
3.10 Deriving the SEX variable
00:02:00
3.11 Obtaining Summary Statistics for SEX
00:03:00
3.12 Concatenating the COUNT and PERCENT Variables
00:03:00
3.13 Deriving the RACE Variable
00:03:00
3.14 Obtaining Summary Statistics for RACE
00:03:00
3.15 Stacking All the 3 Summary Statistics Together
00:06:00
3.16 Fixing the Precision Points
00:04:00
3.17 Transposing Data
00:03:00
3.18 Fixing the Order of Statistical Parameters
00:05:00
3.19 Building the Final Report
00:02:00
3.20 Putting the Final Touches to the Report
00:11:00
Resources
Resources – Clinical Data Analysis with SAS
00:00:00
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