Advanced Statistical Methods in Epidemiology

Learning Outcomes

Knowledge and Understanding

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

• 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).
• Analyse study data of various types including Mantel-Haenszel estimation with hypothesis testing and confidence interval estimation.
• Undertake basic statistical meta-analysis.
• Model complex study data including several exposures and confounders using logistic regression, Poisson and log-linear regression.
• 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.
• 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.
• Ability to use STATA for epidemiological analysis
• Develop your own epidemiologic research in design, data collection and analysis.

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• 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
• To read, understand and critically appraise published epidemiological research.
• Analyse (using STATA) epidemiological study data with the appropriate statistical tools including Mantel-Haenszel estimation and regression modelling.
• 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.

• 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
• Basic statistics for meta-analysis

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.

Resources & Reading list

Textbooks

Clayton D, Hills M (1993). Statistical models in epidemiology. Oxford: Oxford Science Publications.

Jewell NP (2004). Statistics for epidemiology. London: Chapman & Hall/CRC Press.

Woodward M (1999). Epidemiology: Study desig and data analysis. London: Chapman&Hall/CRC.

Assessment strategy

The assessment is summative, and at an individual level.

The pass mark for the module is 50%.

If you have failed the module, you will have the opportunity to submit work at the next referral (re-sit) opportunity. On passing your referrals, your final module mark will be capped at 50%.

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Class practicals

• Assessment Type: Formative
• Feedback:
• Final Assessment:
• Group Work: No

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 Information

Repeat type: Internal & External