This module equips students with the essential skills needed to analyse and interpret data from cohort, case-control and cross-sectional studies. The module is assessed through an analysis of a given dataset and a reporting exercise of methods and findings.
Intended learning outcomes
Upon successful completion of the module, a student will be able to:
- Explain the key statistical and epidemiological concepts which underlie the analysis of epidemiological data.
- Perform analyses of data arising from epidemiological studies, using appropriate computer software (the software used throughout will be Stata, though R-scripts will be made available for some practical sessions).
- Investigate and assess confounding and effect modification/interaction in epidemiological data.
- Interpret appropriately the results of these analyses, taking into account study design issues.
- Write a clear report presenting and interpreting the results of an analysis of epidemiological data.
Session Content
The module covers the following topics:
- Cohort studies: analysis of rates using stratification to investigate confounding and interaction; Kaplan-Meier survival analysis & log-rank test; introduction to Poisson and Cox regression.
- Case-control studies: design issues including selection of controls and matching; analysis of studies using stratification to investigate confounding and interaction.
- Likelihood theory.
- Logistic regression for the analysis of case-control, cross-sectional and fixed-length cohort studies.
- Reporting of results.
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).
Assessment
For their summative assessment, students are asked to undertake a data analysis exercise, similar to that which they undertake towards the end of the taught component of the module. Students are provided with an epidemiological dataset and a specific research question. They are asked to analyse the dataset to address the research question and to prepare a brief report describing their analysis strategy and the results they obtained, and to discuss their results in the light of the methods used to obtain and analyse the data.
The assessment task requires students to demonstrate: the ability to select and apply appropriate statistical methods to a specific problem, including the investigation of confounding and effect modification; the ability to present their analysis strategy and results in an appropriate way; the ability to interpret their findings appropriately in light of the study design and research question. The assessment task thus gives students an opportunity to consolidate their learning and requires students to apply their learning across the whole of the module.
Credits
- CATS: 15
- ECTS: 7.5
Module specification
For full information regarding this module please see the module specification.
This module is primarily intended for students who have attended the Term 1 modules (1) Statistics for EPH (module code 2021) and (2) Extended Epidemiology (module code 2007), and anyone who wishes to acquire further skills in the analysis and interpretation of epidemiological studies.
In particular, students should be familiar with the three major epidemiological study designs, with the concepts of confounding and effect modification/interaction, with the interpretation of confidence intervals and statistical tests, and with the basic data handling commands in Stata.
Students who have not attended Term 1 modules in Extended Epidemiology and Statistics for EPH are strongly recommended to review the equivalent distance learning modules EPM101 (Fundamentals of Epidemiology) and EPM102 (Statistics for Epidemiology) prior to the start of this module. See for more details.
Applications for Term 2 C2 modules are now closed. Please explore our full intensive modules list for modules which may be open for applications.