Advanced Biostatistics for Epidemiology

Module Aims:
This module builds on the “Principles of Biostatistics” module, revising and extending the statistical methods introduced on that module, with a focus on their application to the analysis of data from epidemiological studies and rigorous interpretation of the findings. The module will consider both some of the theoretical aspects of statistical methods and illustrative examples of their application in different contexts (i.e. different research problems across topics from population health sciences).

Module Learning Outcomes:

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

1. Describe, implement, and interpret a range of statistical methods in the context of population health studies.
2. Critically assess, determine, and justify the relevant statistical method that can appropriately answer a specific research question.
3. Describe (though not necessarily implement) potential extensions to standard methods that could be used to account for complexities within a study (arising either by design or after data collection).
4. Interpret and communicate analysis findings to non-specialists, conveying the level of uncertainty in the evidence and any other potential caveats (e.g. bias).

Pre-requisites:

A module covering the fundamentals of biostatistics (i.e. Statistics for HDS or Principles of Biostatistics), data handling and analysis in  R (Applied Data Analysis), and how to formulate research questions and present findings (Research Skills). In particular:

• An understanding of classical hypothesis and significance testing and how to implement various statistical tests.
• An understanding of regression modelling and how to implement and interpret a regression model.
• Familiarity with the basic concepts of study design and sample size calculations.
• Familiarity with the R statistical software.
• Familiarity with the types of research questions of interest in population health science.
• Ability to present findings via written reports and presentations.

Teaching Strategy:

The module will consist of lectures covering the main material, a mixture of theoretical and high-level aspects combined with examples taken from relevant contexts. The lecture material will be further developed in problem classes, specifically how to apply the methods in GNU R and developing skills to critically assess the relevant output.

Assessment:

The module will be assessed via a practical data analysis and answers to a set of exam-style questions based on (a) the practical data analysis and (b) other aspects of the module material. The assessment will take place during the final two days of the module as follows:

Day 1 – Section A: Students will each be given a random selection of a dataset (ie, students will have slightly different datasets to analyse) and will be expected to conduct a series of suitable analysis to complete blank tables and figures.

Day 2  – Section B: Students will be given to a set of questions based on (i) the completed tables and figures from  Section A and (ii) other topics covered in the module.

Students must complete both Sections A and B.

Module Length: 9 days (double module)