Clinical Research Data Management Course
Introduction to clinical data management
Clinical Research Data Management Course
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Foundations of Clinical Research Data Management
An introductory course covering how clinical research data is collected, managed, cleaned, and prepared for analysis while ensuring quality and regulatory compliance.
Protocol Translation and Case Report Form (CRF) Design
Focuses on translating study protocols into structured data requirements and designing clear, accurate Case Report Forms (CRFs) that support efficient and compliant data collection.
This chapter explores the operational aspects of data collection within REDCap, including data entry workflows, validation procedures, user rights management, audit trails, and quality assurance activities that help ensure research data remain reliable and compliant with regulatory standards.
This topic is about ensuring that data is accurate, complete, and consistent. It covers identifying, investigating, and correcting errors or discrepancies in data. It also focuses on timely resolution of data queries to maintain data integrity and reliability.
This topic introduces R, an open-source programming language used for statistical analysis, data management, and visualization. In clinical research, R is applied to clean, analyze, and report data in a reproducible and reliable manner.
This topic introduces data cleaning and preparation in R, focusing on organizing and transforming raw data into a usable format. It covers handling missing values, correcting errors, and structuring data to ensure accurate and reliable analysis.
This topic introduces data analysis in R, focusing on applying statistical methods to explore and interpret data. It covers summarizing data, performing analyses, and generating insights to support clinical research decisions.
This topic is about presenting data in visual formats such as charts and graphs to make it easier to understand. It also covers creating dashboards that organize and display key information for effective monitoring and decision-making.
This topic is about generating clear and accurate reports from data analysis. It also focuses on ensuring that analyses can be consistently reproduced using well-documented methods and scripts.
This topic is about describing and organizing data through clear documentation and metadata. It focuses on defining data elements, structures, and context to ensure data is understandable, consistent, and usable.