The University of Southampton
Courses

MEDI6066 Advanced Statistical Methods in Epidemiology

Module Overview

Aims and Objectives

Module Aims

• Develop your understanding of modern epidemiological modelling including logistic regression, log-linear modelling and Poisson regression to deal with confounding and multiple exposures. • Develop your understanding for basic concepts of time-to-event (survival analysis) including concept of censoring, basic nonparametric and parametric techniques.

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• Demonstrate an understanding of the basic concepts and application of statistical estimation, hypothesis tests and inference to epidemiological data, in particular in the context when adjusting for confounding and effect modification variables.
• Analyse study data of various types including Mantel-Haenszel estimation with hypothesis testing and confidence interval estimation.
• Model complex study data including several exposures and confounders using logistic regression, Poisson and log-linear regression.
• Perform elementary descriptive time-to-event analysis including the Kaplan-Meier estimate of the survivor function and nonparametric tests for comparing two survivor distributions (log-rank test).
• Perform regression models on time-to-event outcomes (Cox’ proportional hazard’s model).
• Use the STATA software to perform all of the above
Transferable and Generic Skills

Having successfully completed this module you will be able to:

• Read critically empirical based research literature.
• Be able to discuss modern quantitative strategies in epidemiological research.
• Develop your own epidemiologic research in design, data collection and analysis.
• Ability to use STATA for epidemiological analysis
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Identify the appropriate statistical tools for a given epidemiological study with a specific design such cross-sectional, cohort or case-control (matched or unmatched).
• Identify the appropriate statistical models given for a given epidemiological study.
• Analyse (using STATA) epidemiological study data with the appropriate statistical tools including Mantel-Haenszel estimation and regression modelling.
• To read, understand and critically appraise published epidemiological research.
• Identify the right tools in STATA to analyse a given epidemiological data set including the interpretation on the various output coefficients and tests provided by STATA

Syllabus

• Introducing STATA for epidemiologists. • Inferential concepts including confidence intervals and hypothesis tests with focus on adjusted measures (MHE). • Confounding, effect modification and Mantel-Haenszel estimation. • Regression and logistic regression. • Log-linear modelling and Poisson regression. • Time-to-event modelling, survival function, hazard function, censoring. • Kaplan-Meier estimation and nonparametric group tests. • Cox’ regression model

Special Features

Special features of this course are the potential for deeper understanding of epidemiological quantitative approaches combined with practical exercises in the very popular STATA package module for epidemiologists.

Learning and Teaching

Teaching and learning methods

A variety of methods will be used including lectures, active participatory methods, case studies of epidemiology in practice, practical exercises using STATA, guided reading, group study and individual study.

TypeHours
Independent Study100
Teaching25
Total study time125

Resources & Reading list

Woodward M. (1999). Epidemiology: Study design and data analysis.

Clayton D, Hills M (1993). Statistical models in epidemiology.

Jewell NP (2004). Statistics for epidemiology.

Assessment

Summative

MethodPercentage contribution
Coursework  (1750 words) 100%

Referral

MethodPercentage contribution
Coursework  (1750 words) 100%

Repeat Information

Repeat type: Internal & External

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