Training on Epidemiological Data Analysis Using Stata

Epidemiologists have relied on Stata for over 30 years because of its specialized epidemiologic commands, accuracy, and ease of use. Whether you are researching infectious diseases, investigating exposure to pathogens, or studying chronic diseases, Stata provides the data management and statistical tools to support your research. It also gives you the ability to make publication-quality graphics so you can clearly display your findings.

Course Duration

5 Days

Who Should Attend

• Epidemiologists
• Public health professionals
• Medical researchers
• Biostatisticians
• Data analysts working in health-related fields

Organizational Impact

• Improve evidence-based public health decision-making through rigorous epidemiological analysis.

• Enhance credibility and accountability with government agencies and donors.

• Foster a data-driven culture to identify health risks and opportunities for better resource allocation.

• Standardize data analysis processes to increase operational efficiency and impact.

Personal Impact

• Gain specialized skills essential for careers in public health and epidemiology.

• Advance toward senior analytical, research, or leadership roles.

• Contribute to public health improvement with high-quality, impactful data insights.

• Build confidence and authority to lead and champion data-driven initiatives.

By the end of this course, participants will:

• Understand the basics of epidemiological data analysis.
• Navigate and use Stata software efficiently.
• Manage and manipulate epidemiological data in Stata.
• Perform descriptive and inferential statistical analyses.
• Conduct regression analysis and interpret the results.
• Apply epidemiological methods to real-world data.
• Present and visualize epidemiological findings effectively.

Module 1: Introduction to Epidemiology and Stata

• Overview of epidemiological concepts
• Introduction to Stata: Interface, commands, and syntax
• Importing and exporting data
• Data cleaning and preparation
• Basic descriptive statistics

Module 2: Data Management and Descriptive Analysis

• Data manipulation techniques
• Creating and managing datasets
• Generating summary statistics
• Exploring data distributions
• Handling missing data

Module 3: Inferential Statistics in Epidemiology

• Hypothesis testing and p-values
• Confidence intervals
• Comparing means (t-tests, ANOVA)
• Non-parametric tests
• Chi-square tests for categorical data

Module 4: Regression Analysis

• Introduction to regression analysis
• Simple and multiple linear regression
• Logistic regression
• Survival analysis
• Model diagnostics and interpretation

Module 5: Advanced Epidemiological Methods and Data Visualization

• Advanced regression techniques (e.g., Poisson regression, Cox regression)
• Handling complex survey data
• Visualizing epidemiological data (graphs, plots)
• Reporting and presenting results
• Practical session: Applying learned techniques to a real dataset