| Unit name | Statistics for Epidemiology |
|---|---|
| Unit code | BRMSM0032 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Jon Heron |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | Bristol Medical School |
| Faculty | Faculty of Health Sciences |
Why is this unit important?
Statistical models underpin all quantitative epidemiology, and are essential for epidemiologists in comprehending, interpreting, and predicting patterns of diseases within populations. This unit will cover how to conduct statistical analyses of epidemiological data and how to interpret their results.
You will learn about statistical methods commonly applied in epidemiology, including linear, logistic, Poisson and Cox regression. These statistical methods enhance the validity and reliability of epidemiological studies, facilitating evidence-based decision-making in public health interventions and policies.
Such methods might be used to quantify the association between exposure to a potential risk factor and the occurrence of a disease, whilst controlling for factors that might distort the true association between an exposure and outcome. Alternatively, using the same models, you might seek to construct, validate, and interpret prediction models in order to address diagnostic and prognostic research questions - instrumental in developing targeted intervention strategies and allocating resources efficiently.
How does this unit fit into your programme of study?
This compulsory unit in Teaching block 2 will develop your skills in using regression models to address applied research questions and interpreting the results. It will build on the regression models that you will be introduced to in the “Introduction to Epidemiology and Statistics” unit, and provide you with the statistical skills and knowledge that you will need to complete your dissertation, particularly if you choose a quantitative topic based on secondary analysis of existing data.
An overview of content
This unit is divided into four distinct components:
How will students, personally, be different as a result of the unit
At the end of this unit, you will have developed the knowledge and skills needed to conduct statistical analyses of epidemiological data and interpret their results. You will have an in-depth conceptual understanding of the regression models most commonly used by epidemiologists, and be able to use these regression models in R.
The skills you acquire will allow you to analyse and interpret data effectively. You will be able to assess risk factors, estimate the likelihood of future disease occurrences and make informed public health recommendations. You will understand the implications of missing data and how to address these - a widespread issue when analysing epidemiological data.
These skills are essential to undertake a further higher degree in Epidemiology, and will also support you to work successfully as an epidemiologist.
Learning Outcomes
All of the learning on this unit will be face-to-face teaching through a mixture of lectures, workshops, and individual and group practical exercises. Opportunities to ask questions and discuss key issues as a group are provided throughout the programme.
You will be assigned homework to support and consolidate your learning. This will include further practical exercises and reading.
Tasks which help you learn and prepare you for summative tasks (formative):
Formative assessments will come in many forms such as informal questioning, quizzes and group exercises in lectures, tutorials and homework. These form an assessment for learning and will not contribute to the final unit mark. R-based elements will be a feature of the majority of the practical sessions, including the one-day prediction “workshop” where students will work (and compete) in teams in an attempt to devise the best prediction model.
Tasks which count towards your unit mark (summative):
The unit will be assessed using three pieces of coursework. Each will consist of a data analysis and interpretation task. Students will be given a data set and a set of analytical tasks to complete:
When assessment does not go to plan
If you do not pass the unit, you will normally be given the opportunity to take a reassessment as per the Regulations and Code of Practice for Taught Programmes. Decisions on the award of reassessment will normally be taken after all taught units of the year have been completed. Reassessment will normally be in a similar format to the original assessment that has been failed.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. BRMSM0032).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
