Epidemiological Data Analysis Using STATA Course

Introduction
The ability to analyze and interpret epidemiological data is essential for evidence-based decision-making and research. The Epidemiological Data Analysis Using STATA Course is designed to equip public health professionals, researchers, analysts, and students with the practical skills needed to manage, analyze, and interpret epidemiological data using STATA a powerful and widely used statistical software.

This hands-on course introduces participants to the foundational and advanced functions of STATA for epidemiological research, including data cleaning, variable management, statistical modeling, and visualization. Participants will learn how to apply key epidemiological methods such as prevalence and incidence estimation, regression analysis, survival analysis, and longitudinal data techniques through real-world datasets and guided exercises.

The course blends theoretical understanding with practical application, allowing participants to critically assess data quality, select appropriate analytical approaches, and generate reproducible results. Special attention is given to interpreting output in the context of public health, ensuring that participants can communicate findings effectively to stakeholders and policy makers.

Whether you are conducting academic research, evaluating health programs, or contributing to surveillance and policy planning, this course provides the essential tools and confidence to perform epidemiological analyses using STATA with accuracy and professionalism.

Who should attend?
This course is ideal for:

• Public health professionals involved in surveillance, program evaluation, or research
• Epidemiologists and biostatisticians seeking practical STATA application skills
• Health program managers and M&E specialists working with population-level data
• Medical and public health researchers conducting clinical, field, or population-based studies
• University faculty in public health, epidemiology, medicine, or biostatistics
• NGO and government staff engaged in data analysis for health policy and planning
• Consultants and analysts working on health-related data projects

Course duration
10 Days

Course objectives

By the end of this course, participants will be able to:

·       Understand and apply key epidemiological concepts including study designs, disease measurement, and risk assessment in the context of public health research.

·       Use STATA software proficiently to manage, clean, manipulate, and analyze epidemiological datasets.

·       Conduct descriptive and inferential statistical analyses using STATA, including hypothesis testing, t-tests, chi-square tests, ANOVA, and non-parametric alternatives.

·       Develop and interpret regression models (linear, logistic, and Cox proportional hazards models) to explore associations between exposures and outcomes.

·       Perform survival analysis using both non-parametric and parametric models to estimate time-to-event outcomes and predict hazard ratios.

·       Calculate and interpret epidemiological measures such as odds ratios, risk ratios, incidence rates, risk differences, and attributable fractions using appropriate statistical techniques.

·       Determine appropriate sample sizes and calculate statistical power for various epidemiological study designs using STATA commands and modules.

·       Create and analyze 2×2 and stratified tables for cohort, case-control, and cross-sectional studies to assess associations and control for confounding.

·       Analyze complex cohort and case-control study data, including matched data, using advanced modeling techniques such as conditional logistic regression and Poisson models.

·       Explore emerging tools and techniques, such as spatial epidemiology, longitudinal data analysis, and automation of STATA workflows for enhanced analytical efficiency.

·       Interpret and present analytical results effectively, translating statistical outputs into clear public health insights for use in research, policy, or programmatic contexts.

·       Apply ethical principles and best practices in epidemiological data management and analysis, with an awareness of privacy, confidentiality, and responsible data use.

Course Content

Module 1: Foundations of Epidemiology and Study Design

• Core concepts and definitions in epidemiology
• Populations vs. samples: representativeness and generalizability
• Measuring disease frequency: incidence, prevalence, and cumulative risk
• Overview of epidemiological study designs:
• Randomized controlled trials
• Cohort studies
• Case-control studies
• Cross-sectional and ecological studies
• Identifying and measuring exposures and outcomes
• Practical exercises using real-world datasets

Module 2: Introduction to STATA for Epidemiological Analysis

• Overview of STATA interface and key commands
• Data import, management, labeling, and cleaning
• Data types and structure: variables, observations, and datasets
• Generating and transforming variables
• Creating reproducible do-files and logs
• Practical exercises: navigating STATA and preparing datasets for analysis

Module 3: Descriptive Statistics and Exploratory Data Analysis

• Summary statistics: mean, median, SD, proportions
• Frequency tables, cross-tabulations, and visualization
• Stratification and subgroup analysis
• Exploring missing data and data quality issues
• Graphing epidemiological data: histograms, boxplots, scatter plots
• Hands-on exercises in data exploration and cleaning

Module 4: Inferential Statistics in Epidemiology

• Introduction to statistical testing
• Chi-square and Fisher’s exact tests
• t-tests and non-parametric alternatives (Mann-Whitney, Wilcoxon)
• ANOVA and ANCOVA for comparing means
• Correlation analysis
• Exercises with STATA output interpretation

Module 5: Regression Models for Epidemiological Data

• Simple and multiple linear regression
• Binary logistic regression and interpretation of odds ratios
• Multivariable model building: confounding, interaction, and model fit
• Use of categorical and continuous predictors
• Practical model diagnostics and residual analysis
• Case-based exercises on interpreting results

Module 6: Sample Size and Power Calculation

• Principles of sample size determination in different study designs
• Effect size, significance level, and statistical power
• Using STATA for sample size and power analysis
• Design effects and weighting
• Constructing valid comparison groups and minimizing bias
• Exercises using sampsi, power, and svy commands

Module 7: Epidemiological Tables and Risk Estimation

• 2x2 and stratified tables for various study designs
• Measures of association: risk ratio, odds ratio, incidence rate ratio
• Risk difference and attributable risk/fraction
• Mantel-Haenszel adjustment and test for homogeneity
• Chi-squared trend analysis
• Interpreting and presenting epidemiological results

Module 8: Survival Analysis

• Introduction to time-to-event data and censoring
• Kaplan-Meier survival curves and log-rank tests
• Cox proportional hazards model and assumptions
• Parametric models: Weibull, exponential, lognormal
• Time-varying covariates and hazard ratios
• Graphical presentation and STATA implementation
• Practical exercises using cohort survival data

Module 9: Advanced Analysis of Cohort and Case-Control Studies

• Cohort analysis: risk estimates and adjusted measures
• Use of sampling weights and complex survey designs
• Variance estimation and standard error adjustment
• Case-control matching techniques and conditional logistic regression
• Case-cohort and nested designs
• Exercises applying real epidemiological datasets

Module 10: Emerging Trends in Epidemiological Data Science

• Introduction to machine learning in epidemiology: when and how to use
• Automating workflows in STATA with loops and macros
• Data visualization and dashboard tools for epidemiologists
• Real-time surveillance and dynamic modeling of outbreaks
• Data privacy and ethical issues in digital health analytics

Module 11: Longitudinal and Panel Data Analysis      

• Repeated measures vs. longitudinal design
• Fixed-effects vs. random-effects models
• Handling autocorrelation and time-lagged effects
• Using xt commands in STATA for panel data
• Practical applications in public health and clinical studies

Module 12: Spatial Epidemiology and Geographic Data Analysis

• Basics of spatial data and geocoding health data
• Introduction to STATA’s spmap and integration with GIS tools
• Analyzing geographic patterns of disease
• Hotspot detection and spatial autocorrelation
• Mapping incidence and risk estimates

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808 

Terms of Payment

Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better