Statistical methods in veterinary epidemiology
The module is designed to provide you with the key statistical knowledge, understanding and skills you will need to analyse and interpret data from common forms of epidemiological studies that are conducted in veterinary science.
Section 1 Introduction to Multivariable Analysis of Epidemiological Data
In the first section of the module the focus is on multivariable epidemiological analysis. In sessions 1–6 you will go through a series of epidemiological concepts that are essential for the later application of specific multivariable techniques, the focus of Sections 2 and 3. This section looks at the use of measures of effect and the analysis of cohort and case–control studies, and introduces the concepts of likelihood and the analysis of time to event data.
Section 2 Logistic Regression
The second section deals with logistic regression, which is used to model binary outcome data. The three sessions explain how to deal with interaction in a logistic regression model and how to use and interpret logistic regression for quantitative exposures.
Section 3 Regression Models for Time to Event Data
Following the introduction to multivariable epidemiological analysis and statistical modelling in Section 1, and the study of logistic regression to model binary outcome data in Section 2, this final section allows you to study regression models for time to event data: Poisson regression and Cox regression.
By the end of this module you should be able to:
- describe the basic statistical measures and concepts underlying the analysis of epidemiological data.
- use a comprehensive set of statistical methods suitable for a wide range of epidemiological situations.
- select appropriate statistical techniques for the analysis of data from epidemiological studies.
- identify specific issues relevant to case–control and cohort studies.
- perform basic statistical modelling techniques.
- investigate confounding and interaction in epidemiological data using both stratified analyses and statistical modelling methods.
- interpret the results of statistical procedures and draw appropriate conclusions.
Your work for this module will be assessed by means of a three-hour unseen written examination paper. In addition, you are required to complete and submit at least one tutor-marked assignment (TMA) for assessment.
The grade awarded will be based on the mark obtained in the written examination (80%) and on the mark for the compulsory TMA (20%).
Module information sheets
Download the full module information sheet from the RVC website.