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This module will provide training in statistical methods and their application to address research questions focused on infectious diseaseÌýepidemiological problems. Students will apply concepts in practical sessions, in which they will learn to use statistical software and to critically appraise the methods that they are applying, and interpret results in the context of infectious disease epidemiological questions. This second of two Applied Statistics for Infectious Disease Epidemiology modules will build on core statistical concepts to focus on more advanced skills in data manipulation and analysis, including methods to explore associations with continuous outcomes, analyse time-to-event data in determining the risk of an event, and analyse repeat measures. At the end of this module, students will be able to discuss and plan a series of analyses aimed at effectively addressing a specificÌýinfectious disease epidemiological research question, taking into account limitations of observational data, and to interpret and appraise the results of statistical analyses they encounter in the literature.

By the end of the module, students should be able to:

1. Prepare a complex dataset for analysis consideringÌýadvancedÌýissues around data cleaning and missing data, building on data manipulation skills from Applied Statistics for ID Epi 1.
2. Understand and apply regression models to explore associations with count outcomes.
3. Understand and apply methods to incorporate time-to-event data in determining the risk of an event, interpret and appraise results from these analyses.
4. Understand and apply methods to analyseÌýlongitudinal follow-up data, including repeated measures and time-updated covariates,Ìýand hierarchical data accounting for clustering within groups.
5. Discuss alternative approaches to measuring mediation processes.
6. Understand the kinds of missingness typically present in datasets and how to address them in analysis, including via the use of imputation.
7. Understand sampling challenges and consider potential approaches to conducting epidemiological research with marginalised populations and others lacking sampling frames.
8. Have insight into the development and validation of prognostic scores/risk models and be able to interpret these.
9. Critically appraise various randomized and quasi-randomized approaches to evaluating infectious disease outcomes
10. Incorporate understanding of confounding and effect modification from Applied Statistics for ID Epi 1 into analytical approaches.
11. Discuss and plan a series of analyses to addressÌýa specific infectious disease epidemiology research question, taking into account limitations of observational data. Understand the role of and incorporate plans for sensitivity analyses.
12. Build on skills from Applied Statistics for ID Epi 1 to effectively present statistical results in tabular, graphicalÌýand written form, including for a non-statistical audience.

This is a compulsory module for students in the MSc Applied Infectious Disease Epidemiology degree.