Unit information: Introduction to Epidemiology and Statistics in 2019/20

Unit name	Introduction to Epidemiology and Statistics
Unit code	BRMSM0001
Credit points	20
Level of study	M/7
Teaching block(s)	Teaching Block 1 (weeks 1 - 12)
Unit director	Dr. Stephanie MacNeill
Open unit status	Not open
Pre-requisites	None
Co-requisites	None
School/department	Bristol Medical School
Faculty	Faculty of Health Sciences

Description including Unit Aims

The aims of this unit are to:

- Give students an appreciation of the uses of epidemiology, and the role of statistical methods in epidemiology and public health. - Convey an understanding of measures of disease frequency, measures of effect, and measures of public health impact - Convey an understanding of sampling variation, how to quantify uncertainty using confidence intervals, and appropriate and inappropriate interpretations of p values - Introduce randomized and non-randomized (observational) study designs and explain their strengths and limitations -Introduce linear regression for analyses of numerical outcomes, and logistic regression for analyses of binary outcomes - Introduce the concept of confounding, and explain how it can be addressed during study design and through statistical analysis. -Explain how selection and information bias can occur in epidemiological studies -Explain the importance of effect modification, and how it can be examined in statistical analyses - Give students knowledge of the uses of regression models to estimate exposure effects after controlling for confounders, and for multivariable prediction - Introduce students to deriving causal inferences from epidemiological studies

Intended Learning Outcomes

By the end of this unit, students should be able to:

1. Calculate and interpret measures of disease frequency, association and effect
2. Describe the principles of different study designs for addressing a specific epidemiological question
3. Evaluate the strengths and limitations of different epidemiological study designs
4. Explain sampling variation, and use confidence intervals and p-values in interpreting the results of statistical analyses
5. Explain the concept of confounding and apply methods to address it
6. Explain the concept of effect modification and apply methods to address it
7. Explain how selection and information biases can affect different types of study
8. Use Stata software to fit linear and logistic regression models, and interpret output from these
9. Evaluate the strength of evidence supporting a causal link between an exposure and an outcome

Teaching Information

There will be 10 teaching weeks, plus reading week and revision week.

Face to face teaching for a total of 50 hours will include lectures and tutorials. Directed and self-directed learning (150 hours) will include activities such as reading, quizzes, multi-media learning and preparation for assessment.

Assessment Information

Assessment for learning will be provided via weekly homework which will be available after teaching. Feedback in the form of model answers will be provided. (ILOs 1- 9). Homework is self-directed learning and will not contribute to final unit mark.

There will be two progress tests which will comprise unseen MCQs and short answer questions in exam conditions (each will be 1 hour). They will prepare students for the final summative assessment. Feedback in the form of correct/ model answers, with explanation, will be provided. The progress test is for student learning and does not contribute to the final mark for the unit (ILOs 1- 8).

Summative exam at end of unit. A 2-hour closed book exam will comprise MCQs and short-answer (100% of total unit mark) (ILOs 1-9). A mark of 50% is required to pass the unit.

Reading and References

There is no compulsory unit text book.

Recommended reading:

Webb, P., Bain, C., Page, A. (2017) Essential Epidemiology 3rd ed. Cambridge University Press

Rothman, K., Lash, T., Greenland, S., Williams, L. (2008) Modern Epidemiology 3rd ed. Wolters Kluwer

Kirkwood, B.R., Sterne, J. (2010) Essential Medical Statistics. Blackwell.

Petrie, A., Sabin, C. (2009) Medical Statistics at a Glance. 3rd ed. Wiley-Blackwell.