The aim of the module is to equip students with the necessary skills to understand and appraise the design, analysis and interpretation of epidemiological studies. The module covers both the design and statistical analysis of epidemiological studies. It is designed for students who want to improve their understanding of the methods used in public health research.
The first half of the module is focused on design aspects and key epidemiological concepts. In this part of the course, students learn the strengths and weaknesses of the different designs and how to choose an appropriate sample size. They are also introduced to the concepts of confounding and selection bias through the use of causal diagrams.
The second half of the module focuses on the use of regression models to adjust for confounding. The statistical concept of clustering is also introduced in this part of the course.
Students will have the opportunity to analyse data in a number of computer-based practical classes. However, the emphasis in these classes, and throughout the course, is on understanding epidemiological concepts rather than gaining statistical expertise.
Intended learning outcomes
Upon successful completion of the module, a student will:
- Be familiar with the main study designs used in epidemiological research, and understand their advantages and disadvantages.
- Understand the concepts of confounding, statistical interaction and clustering.
- Understand why statistical models are used in epidemiology.
- Be able to interpret the output from a logistic regression model.
- Be able to critically appraise the design, analysis and interpretation of studies conducted by other investigators, and communicate effectively with public health researchers.
Session Content
The module will cover the following topics:
- Epidemiological study designs
- Measures of disease
- Sample size calculations
- Selection bias
- Confounding
- Statistical interaction
- Logistic and linear regression
- Clustering
- Paper critique
Mode of delivery
This module is delivered predominantly face-to-face. Where specific teaching methods (lectures, seminars, discussion groups) are noted in this module specification these will be delivered by predominantly face-to-face sessions. There will be a combination of live and interactive activities (synchronous learning) as well as self-directed study (asynchronous learning).
The course material will be delivered through lectures, guided self-study and tutor-led practical sessions. The practical sessions are problem-based, with some involving pen and paper calculation or use of statistical software (primarily Stata but no previous Stata experience is expected). Students are encouraged to work in groups and will have the opportunity to present their work.
Assessment
The assessment for this module has been designed to measure student learning against the module's intended learning outcomes (ILOs) as listed above. Formative assessment methods may be used to measure students’ progress. The grade for summative assessment only will go towards the overall award GPA. The summative assessment will involve a written review of a paper from the public health literature. The student will answer a series of questions designed to test understanding of the study design and statistical methods used including potential sources of bias, interpretation of results, and strengths and weaknesses of the study.
The assessment for this module will be submitted online. Students are expected to complete the assessment during the last two weeks of the module (teaching weeks 4 and 5 of term 2).
Credits
- CATS: 15
- ECTS: 7.5
Module specification
For full information regarding this module please see the module specification.
The module is intended for students who have attended Term 1 modules in Epidemiology and Statistics, and who wish to understand more about the design and analysis of epidemiological studies. It includes some review and consolidation of Term 1 material.
Applications for Term 2 C2 modules are now closed. Please explore our full intensive modules list for modules which may be open for applications.