Data Management and Analysis in Clinical Trials
( 25 Modules )

Module #1
Introduction to Clinical Trials
Overview of clinical trials, importance, and phases

Module #2
Data Management in Clinical Trials
Importance of data management in clinical trials, data types, and data flow

Module #3
Regulatory Requirements for Data Management
ICH-GCP, FDA, and EMA regulations for data management in clinical trials

Module #4
Data Management Plans (DMPs)
Creating a DMP, content, and importance

Module #5
Data Quality and Quality Control
Ensuring data quality, quality control measures, and data cleaning

Module #6
Data Collection Methods
Types of data collection methods, advantages, and limitations

Module #7
Electronic Data Capture (EDC) Systems
Overview of EDC systems, benefits, and challenges

Module #8
Data Storage and Backup
Data storage options, data backup strategies, and data archiving

Module #9
Database Design and Development
Designing and developing databases for clinical trials

Module #10
Data Integration and Interoperability
Integrating data from different sources, data standards, and interoperability

Module #11
Introduction to Biostatistics
Basic concepts of biostatistics, descriptive statistics, and inferential statistics

Module #12
Data Analysis Techniques
Descriptive and inferential statistical techniques, hypothesis testing, and confidence intervals

Module #13
Data Visualization
Data visualization techniques, types of plots, and best practices

Module #14
Biostatistical Software
Overview of biostatistical software, such as R, SAS, and Python

Module #15
Exploratory Data Analysis (EDA)
Conducting EDA, data summarization, and data exploration techniques

Module #16
Survival Analysis
Introduction to survival analysis, Kaplan-Meier estimates, and Cox proportional hazards model

Module #17
Mixed Effects Models
Introduction to mixed effects models, linear mixed effects models, and generalized linear mixed models

Module #18
Machine Learning and Predictive Analytics
Introduction to machine learning, supervised and unsupervised learning, and predictive analytics

Module #19
Meta-Analysis
Conducting meta-analyses, forest plots, and publication bias

Module #20
Advanced Statistical Modeling
Generalized linear models, generalized estimating equations, and Bayesian statistics

Module #21
Data Management Best Practices
Data management workflows, data quality control, and data validation

Module #22
Data Analysis Best Practices
Data analysis workflows, data visualization best practices, and results interpretation

Module #23
Risk-Based Monitoring
Risk-based monitoring, central monitoring, and on-site monitoring

Module #24
Data Governance and Security
Data governance, data security, and data protection regulations

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Data Management and Analysis in Clinical Trials career

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Data Management and Analysis in Clinical Trials ( 25 Modules )

Data Management and Analysis in Clinical Trials
( 25 Modules )