Epidemiological Methods

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Students should be able to apply appropriate statistical analysis techniques to analyse epidemiological data
• Students should be able to anticipate particular problems that may arise in epidemiological data
• Students should be aware of the effects that chance, confounding and bias may have on epidemiological studies
• Students should be able to distinguish between different types of study design

Lecture 1 – Introduction to Epidemiology Definition of epidemiology Basic epidemiological concepts Epidemiological study design Developmental Origins of Health and Disease (DOHaD) Lecture 2 – Bias, Confounding and Interactions The role of chance Types of bias The concept of confounding Interactions in epidemiological research Lecture 3 – Establishing causality Defining causes of disease How to draw and interpret causal diagrams Using DAGitty The Bradford Hill criteria Lecture 4 – Ratio Measures of Association Defining risks Prevalence and incidence Measures of association When not to use an odds ratio Lecture 5 – Case-control studies 1 Design of case-control studies Design issues Choice of cases and controls Matching Collection of exposure information Basic analysis of unmatched studies Test for homogeneity of odds ratios Mantel-Haenszel methods for stratified analysis 2xk tables Test for trend Basic analysis of matched studies Lecture 6 – Case-control studies 2 Logistic regression - unconditional and conditional Example of analysis using Stata Lecture 7 – Cohort Studies 1 Retrospective and prospective cohort studies Follow-up using official statistics Analyses using an external standard Calculating Standardised Mortality Ratios (SMRs) Lecture 8 – Cohort Studies 2 Poisson generalised linear models to test for trend in SMRs Cox Proportional Hazards model Lecture 9 – Design of Epidemiological Studies A practical session to design a study to assess whether use of mobile phones increases the risk of brain cancer. Lecture 10 – Ecological Studies and Meta-analysis Definition of ecological studies Ecological fallacy Types of ecological studies Definition of meta-analysis Conducting a meta-analysis Fixed and random-effects models Forest plots Lecture 11 – Hints and Tips Data cleaning Missing data Categorisation of continuous variables Significance testing Transforming data Presentation of results Lecture 12 – Case Study A practical session discussing an investigation into a cluster of leukaemias near the Sellafield nuclear site in West Cumbria

Learning is through twelve 45 minute lectures and a two-hour Stata tutorial.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.