This short course teaches epidemiologic study design and biostatistics side by side. It reaches people the degree tracks miss: clinicians, graduate students, and public-health staff who want a practical grounding without enrolling in a program. All practicals are worked in R, using the same material published on this site.
Draft proposal. This is an early sketch of a proposed short course. The course number, dates, fees, and daily schedule are placeholders and will be settled before the course is first offered.
Overview
IDE xxx (proposed) is a non-credit short course that pairs the core ideas of epidemiology with the statistics used to measure them. Participants learn how to describe health and disease in populations, choose a study design, guard against bias and confounding, and fit and interpret the regression models that appear throughout applied epidemiology. The material is taught with worked R examples throughout.
By the end of the course, participants will be able to:
- Describe how health and disease are measured in populations
- Choose an appropriate study design for a given question
- Recognize and address bias and confounding
- Calculate and interpret measures of effect and impact
- Fit and interpret linear and logistic regression models in R
- Read the results of a systematic review and meta-analysis
Who should apply
The course is aimed at clinicians, graduate students, and public-health practitioners who want a working grounding in epidemiology and statistics.
Prerequisites. No prior epidemiology or statistics is assumed. Participants should be comfortable using a laptop and willing to work through hands-on R exercises; no prior R experience is required, though a little helps.
Format and delivery
- Duration. 2 to 3 weeks.
- Mode. Hybrid. Attend in person or join online.
- Daily hours. About 2 hours of lecture and about 3 hours of tutored practical work per day.
- Assessment. No formal assessment.
- Certificate. Participants who attend receive a certificate of attendance.
- Equipment. Participants bring their own laptops with R and RStudio installed. Setup instructions are sent before the course begins.
Course content and topics
- Measuring health and disease
- Data and distributions
- Study designs: cross-sectional, cohort, case-control, intervention, and ecological
- Bias and confounding
- Measures of effect and impact
- Statistical inference
- Categorical data
- Linear regression
- Logistic regression and generalized linear models
- Sampling and sample size
- Introduction to infectious-disease epidemiology
- Systematic reviews and meta-analysis
Day-by-day timetable
The course runs over two to three weeks. A representative two-week schedule is shown below; a three-week offering spreads the same material over more days with additional practical time.
Week 1
| Day | Morning lecture | Afternoon practical (R) |
|---|---|---|
| 1 | Measuring health and disease | Reading data and summarizing distributions |
| 2 | Data and distributions | Describing data and simple summaries |
| 3 | Study designs I: cross-sectional and cohort | Rates, risks, and person-time |
| 4 | Study designs II: case-control, intervention, ecological | Odds ratios and case-control analysis |
| 5 | Bias and confounding | Stratified analysis and adjustment |
Week 2
| Day | Morning lecture | Afternoon practical (R) |
|---|---|---|
| 6 | Measures of effect and impact | Attributable and population-level measures |
| 7 | Statistical inference | Confidence intervals and hypothesis tests |
| 8 | Categorical data and linear regression | Fitting and checking linear models |
| 9 | Logistic regression and generalized linear models | Fitting logistic models |
| 10 | Sampling, sample size, and evidence synthesis | Sample size; reading a meta-analysis |
Site resources
The course draws on material already published on this site. Participants can read ahead or review afterward.
- Statistical inference
- Hypothesis testing
- Confidence intervals
- Linear regression
- Logistic regression
- Generalized linear models
- Diagnostic testing
- Survey sampling
- Multiple testing
Two further topics, study designs and measures of association and impact, are planned as site pages and will be linked here once published.
See Programs for how this short course fits alongside the degree tracks and other offerings.
Fees and how to apply
Fees. @placeholder
How to apply. @placeholder
AI and academic integrity
Large language models such as ChatGPT, Claude, and Gemini can support your learning, and you are welcome to use them. If you do, cite the tools you used and describe how you used them. These tools do not replace your own understanding of the material, and you remain responsible for the accuracy of your work and any citations. Using them without attribution is plagiarism.
Proposal change notice
This is a draft proposal. Its content, structure, dates, and fees are subject to change before the course is offered.