About This Course
Epidemiological data analysis sits at the heart of evidence-based public health decision-making. Whether investigating disease outbreaks, evaluating health interventions, or modelling disease burden across populations, the ability to correctly manage and analyse health data is a non-negotiable competency for today's public health workforce. Stata — one of the most widely used statistical software packages in epidemiology and clinical research — provides a powerful, flexible environment for the full analytical pipeline, from raw data cleaning to publication-ready outputs.
This ten-day TrainingComplex programme is designed for professionals who work with health data and need to build or sharpen their Stata-based analytical skills within an epidemiological framework. The course moves systematically from foundational Stata navigation and data management through to advanced topics including logistic and Poisson regression, survival analysis, meta-analysis, and the analysis of complex survey data. Every concept is grounded in applied examples drawn from real epidemiological contexts — infectious disease surveillance, longitudinal cohort studies, case-control designs, and national health surveys.
Beyond technical skills, the programme emphasises critical thinking around study design, confounding, effect modification, and the valid interpretation of statistical outputs. Participants leave not only knowing how to run commands, but understanding what their results mean and how to communicate findings with precision to scientific and policy audiences. With guided practice on curated datasets, individual exercises, and a capstone analytical project, this course delivers the hands-on confidence that classroom lectures alone cannot.
TrainingComplex delivers this programme in a supportive, collaborative environment where participants from diverse professional backgrounds — epidemiologists, biostatisticians, programme evaluators, and health researchers — learn from each other as much as from the facilitator. All participants receive annotated Stata do-files, reference datasets, and a comprehensive course manual that continues to serve as a desk reference long after the training ends.
Who Should Attend
- Epidemiologists and public health officers working with disease surveillance or outbreak data
- Biostatisticians and health data analysts in ministries of health, research institutes, or NGOs
- Clinical researchers and medical officers involved in health studies or trials
- Monitoring and evaluation specialists working on health programmes
- PhD and postgraduate students in public health, epidemiology, or related disciplines
- Health policy analysts who need to interrogate and interpret quantitative evidence
- Global health professionals requiring proficiency in Stata for grant-funded research projects
Learning Objectives
- Navigate the Stata interface confidently and write clean, reproducible do-files for epidemiological workflows
- Import, clean, reshape, and merge health datasets from multiple sources including surveys and registers
- Compute and present descriptive epidemiological statistics including incidence rates, prevalence, and disease burden measures
- Apply inferential statistics — including chi-square, t-tests, and ANOVA — appropriately to epidemiological questions
- Conduct stratified analysis and assess confounding and effect modification using Mantel-Haenszel methods
- Build and interpret simple and multiple logistic regression models for case-control and cross-sectional data
- Run Poisson and negative binomial regression for count and rate data in cohort and surveillance studies
- Perform survival analysis including Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression
- Analyse data from complex multi-stage surveys using Stata's svy commands, accounting for sampling weights and clustering
- Conduct and present a meta-analysis of epidemiological studies using Stata's metan and metafunnel commands
- Produce publication-quality tables, graphs, and forest plots that meet journal and reporting standards
- Interpret regression outputs critically, including odds ratios, rate ratios, hazard ratios, confidence intervals, and p-values
- Design and execute an end-to-end epidemiological data analysis project from raw data to a structured analytical report
Course Modules
Module 1: Introduction to Stata and the Epidemiological Analysis Workflow
- Navigating the Stata interface: Command window, Results, Variables, and Do-file editor
- Understanding Stata file types: .dta, .do, .log, and .ado files
- Writing clean, annotated, and reproducible do-files from the outset
- Setting up a project folder structure for epidemiological research
- Basic Stata syntax: commands, options, if/in qualifiers, and by-prefixes
- Getting help in Stata: help files, search command, and online resources
Module 2: Data Import, Inspection, and Cleaning
- Importing data from Excel, CSV, and other formats using import and insheet
- Inspecting datasets with describe, codebook, list, and summarize
- Identifying and handling missing values, outliers, and inconsistencies
- Recoding, labelling, and generating new variables for epidemiological use
- Detecting and removing duplicate records using duplicates commands
- Documenting data cleaning decisions with comments and log files
Module 3: Data Management and Reshaping
- Merging datasets using one-to-one and many-to-one merge operations
- Appending datasets from multiple time periods or sites
- Reshaping data between wide and long formats with reshape
- Collapsing data to aggregate-level summaries using collapse
- Egenerating date variables and calculating person-time and follow-up duration
- Managing panel and longitudinal health datasets with xtset
Module 4: Descriptive Epidemiology in Stata
- Computing and tabulating frequency distributions and proportions
- Calculating measures of central tendency, spread, and percentiles for continuous variables
- Estimating prevalence, incidence rates, and attack rates from epidemiological data
- Constructing epidemic curves and age-sex pyramids using Stata graphics
- Building two-way and stratified frequency tables with tabulate and table
- Producing formatted summary tables for reports using estout and tabstat
Module 5: Inferential Statistics for Epidemiological Comparisons
- Selecting the appropriate test based on data type and study design
- Conducting chi-square and Fisher's exact tests for categorical associations
- Applying independent and paired t-tests for continuous outcome comparisons
- Using one-way ANOVA and post-hoc tests for multi-group comparisons
- Calculating confidence intervals for proportions, means, and rate differences
- Interpreting p-values, statistical significance, and practical significance in public health context
Module 6: Measures of Association and Stratified Analysis
- Computing relative risk, odds ratios, and rate ratios with confidence intervals using cs, cc, and ir commands
- Understanding confounding: definition, detection, and its implications in observational studies
- Performing stratified Mantel-Haenszel analysis to control for confounders
- Assessing effect modification and interaction in stratified tables
- Comparing crude and adjusted estimates to evaluate confounding and modification
- Interpreting pooled Mantel-Haenszel odds ratios and rate ratios
Module 7: Logistic Regression for Binary Outcomes
- Conceptual foundations of logistic regression in case-control and cross-sectional studies
- Running simple and multiple logistic regression using logit and logistic in Stata
- Selecting and entering covariates: purposeful selection and stepwise approaches
- Interpreting odds ratios, confidence intervals, and model fit statistics
- Assessing model assumptions: linearity in the logit, multicollinearity, and influential observations
- Presenting logistic regression results in publication-ready tables using outreg2 or esttab
Module 8: Poisson and Negative Binomial Regression for Count and Rate Data
- When to use Poisson regression: count outcomes and rate data in cohort and surveillance studies
- Running Poisson regression with and without an exposure offset using poisson
- Interpreting incidence rate ratios and assessing goodness of fit
- Detecting and addressing overdispersion using nbreg for negative binomial regression
- Modelling zero-inflated count data with zip and zinb commands
- Applying Poisson regression to disease mapping and surveillance trend analysis
Module 9: Survival Analysis in Epidemiology
- Core concepts: censoring, time-to-event data, and the survival function
- Declaring survival data in Stata using stset and checking with stdes
- Estimating and plotting Kaplan-Meier survival curves with sts graph
- Comparing survival between groups using the log-rank and Wilcoxon tests
- Fitting Cox proportional hazards models with stcox and interpreting hazard ratios
- Testing the proportional hazards assumption using Schoenfeld residuals and phtest
Module 10: Analysis of Complex Survey Data
- Understanding survey design features: stratification, clustering, and sampling weights
- Declaring survey design in Stata using svyset with appropriate weight and cluster variables
- Running descriptive analyses adjusted for complex sampling with svy: mean and svy: proportion
- Conducting regression analyses on survey data using svy: logistic and svy: poisson
- Interpreting design effects (DEFF) and their implications for standard errors
- Working with Demographic and Health Survey (DHS) and STEPS survey datasets in Stata
Module 11: Meta-Analysis Using Stata
- Principles of systematic review and meta-analysis in an epidemiological context
- Pooling effect estimates from multiple studies using the metan command
- Producing and interpreting forest plots for odds ratios, relative risks, and mean differences
- Assessing heterogeneity using I² statistics, Q-tests, and prediction intervals
- Detecting and addressing publication bias with funnel plots and Egger's test
- Conducting subgroup and sensitivity analyses to explore sources of heterogeneity
Module 12: Data Visualisation and Reporting Standards
- Principles of effective epidemiological data visualisation for scientific and policy audiences
- Creating scatter plots, bar charts, box plots, and line graphs using Stata's twoway suite
- Customising graph aesthetics: titles, labels, legends, colours, and scheme options
- Exporting publication-quality figures in TIFF, PNG, and EMF formats
- Applying STROBE and CONSORT reporting guidelines to observational and trial data respectively
- Compiling analytical results into a structured epidemiological report with clearly labelled tables and figures
Module 13: Capstone Project: End-to-End Epidemiological Analysis
- Selecting and reviewing an assigned epidemiological dataset and formulating analysis objectives
- Developing a structured analysis plan aligned to the study design and research questions
- Executing the full analytical pipeline in a reproducible, annotated do-file
- Applying appropriate descriptive, inferential, and regression methods to answer each objective
- Interpreting results critically, acknowledging limitations, and drawing evidence-based conclusions
- Presenting findings to peers and facilitator with a polished slide deck and written analytical report
Requirements & Prerequisites
Participants should have a working knowledge of basic epidemiological concepts such as incidence, prevalence, relative risk, and odds ratios. Prior exposure to any statistical software — including Excel, SPSS, R, or Stata — is beneficial, though not mandatory, as the course begins from Stata fundamentals. Each participant must bring a laptop with Stata version 15 or later installed; a temporary licence can be arranged through TrainingComplex for those who do not have access.
Training Methodology
- Structured Stata live-coding sessions where the facilitator demonstrates each command in real time and participants replicate the analysis on their own machines
- Guided exercises using curated epidemiological datasets from infectious disease surveillance, cohort studies, and national health surveys
- Case-based problem sets that present a realistic epidemiological scenario and require participants to select, apply, and interpret the appropriate analytical approach
- Annotated do-file workshops where participants review, debug, and improve pre-written Stata scripts to reinforce best coding practices
- Small-group analytical challenges in which teams analyse a shared dataset from different study design perspectives and present their findings
- Peer review of outputs — tables, graphs, and regression results — against reporting standards such as STROBE and CONSORT
- Individual reflection exercises linking each day's statistical methods back to participants' own datasets or current research questions
- Capstone analytical project in which each participant conducts an end-to-end analysis of an assigned dataset and submits a structured analytical report with do-file
Organisational Benefits
- Strengthened internal capacity to conduct rigorous, reproducible epidemiological analyses without reliance on external consultants
- Improved quality and credibility of health research outputs submitted for publication or policy use
- Faster and more accurate turnaround of disease surveillance reports and outbreak investigations
- Enhanced M&E capacity for health programmes through better measurement of health outcomes and intervention effects
- Reduced analytical errors arising from data mismanagement or inappropriate statistical methods
- Greater competitiveness in securing research grants that require demonstrated quantitative analytical capacity
- A workforce able to critically appraise externally produced epidemiological evidence and apply it to local decision-making
- Standardised analytical workflows through shared do-files, naming conventions, and documentation practices
Where Is Epidemiological Data Analysis Using Stata Training Offered?
This course runs in-person across: Dubai, Mombasa, Nairobi, Abuja, Nakuru, Kigali, Lagos, Riyadh, Cape Town, Zanzibar, Addis Ababa, Accra, Kampala, Naivasha, Johannesburg, Pretoria, Kisumu, Arusha, Dar es Salaam, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Live Virtual |
Online |
24 Oct 2026 – 13 Dec 2026 |
KES 160,000 |
| Dubai, United Arab Emirates |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 8,200 |
| Mombasa, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 240,000 |
| Nairobi, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 230,000 |
| Abuja, Nigeria |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 5,600 |
| Nakuru, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 240,000 |
| Kigali, Rwanda |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 3,800 |
| Lagos, Nigeria |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 5,000 |
| Riyadh, Saudi Arabia |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 9,000 |
| Cape Town, South Africa |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 7,600 |
| Zanzibar, Tanzania |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 5,000 |
| Riyadh, Saudi Arabia |
In-Person |
2 Nov 2026 – 11 Nov 2026 |
USD 9,000 |
| Nairobi, Kenya |
In-Person |
2 Nov 2026 – 13 Nov 2026 |
KES 230,000 |
| Live Virtual |
Online |
2 Nov 2026 – 13 Nov 2026 |
KES 160,000 |
| Addis Ababa, Ethiopia |
In-Person |
2 Nov 2026 – 13 Nov 2026 |
USD 4,800 |
+227 more dates — view live schedule →
Frequently Asked Questions
Do I need prior experience with Stata to attend this course?
No prior Stata experience is required. The course begins with a thorough introduction to the Stata interface and syntax before progressing to more advanced methods. However, participants should have a basic conceptual understanding of epidemiology, as the programme focuses on applying Stata to epidemiological problems rather than teaching epidemiology from scratch.
What version of Stata do I need, and will it be provided?
Participants need Stata version 15 or later installed on their laptops before the course begins. If you do not have a licence, TrainingComplex can assist in arranging a short-term Stata licence prior to the start date — please contact us at least two weeks in advance to make this arrangement.
Will I receive any course materials to take away?
Yes. All participants receive a comprehensive course manual, annotated Stata do-files for every session, curated practice datasets, and a set of reference checklists and reporting checklists aligned to STROBE and CONSORT guidelines. These materials are designed to serve as a practical desk reference in your ongoing research work.
Is a certificate issued upon completion?
Yes. Participants who attend at least 80% of the sessions and complete the capstone analytical project receive a Certificate of Completion from TrainingComplex, which can be included in professional portfolios, CVs, and grant applications as evidence of advanced quantitative analytical training.
Can I bring my own dataset to analyse during the course?
Participants are strongly encouraged to bring their own datasets for use during the reflection exercises and, where feasible, the capstone project. Doing so maximises the immediate applicability of skills learned. TrainingComplex also provides curated epidemiological datasets for participants who do not have access to their own data.
