# Unit information: Introduction to Epidemiology and Statistics in 2020/21

Unit name Introduction to Epidemiology and Statistics BRMSM0001 20 M/7 Teaching Block 1 (weeks 1 - 12) Dr. Stephanie MacNeill Not open None None Bristol Medical School Faculty of Health Sciences

## Description

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 details

Teaching will include learning activities set by the tutor including lectures (synchronous and asynchronous), small group work, discussions, individual tasks, and practical activities (face to face or online).

Directed and self-directed learning will include activities such as reading, accessing web-based supplementary materials, critical analysis and completion of assessments.

## Assessment Details

Formative assessment: Weekly homework which will be available after teaching. Feedback in the form of model answers will be provided.

There will be two progress tests which will comprise unseen MCQs and short answer questions available online for students to complete in their own time. They will allow students to assess their own progress and identify where further study is needed. The short answer questions 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.

Summative assessment: The unit will be assessed using a single piece of coursework:

• Series of short-answer questions (responses totalling up to 5000 words) relating to topics covered during the entire unit (ILOs 1-9; 100% of total unit mark).

A mark of 50% is required to pass the unit.

There is no essential unit text book.