Infectious Disease 'Omics

Course overview: Infectious diseases, such as HIV-AIDS, malaria, pneumonia and tuberculosis, account for 25% of global mortality and more than half of all deaths in children under the age of five years. The genetic epidemiology of these diseases can be complex, especially as they may involve several genomes, including the host, pathogen(s) and a vector. There is also a need to look beyond the genome to consider other ‘omes, such as the transcriptome, in a more systems biology framework. To take full advantage of new ‘omic technologies requires the ability to analyse large amounts of data using methods from bioinformatics, population genetics and statistics – the focus of this course. 

Find out more about the course and how to apply. [Applications close on 4 November 2023]

Statistical analysis with missing data using multiple imputation and inverse probability weighting

Course overview: Missing data frequently occurs in both observational and experimental research. They lead to a loss of statistical power, but more importantly, may introduce bias into the analysis. In this course we adopt a principled approach to handling missing data, in which the first step is a careful consideration of suitable assumptions regarding the missing data for a given study. Based on this, appropriate statistical methods can  be identified that are valid under the chosen assumptions. The course will focus particularly on the practical use of multiple imputation (MI) to handle missing data in realistic epidemiological and clinical trial settings, but will also include an introduction to inverse probability weighting methods and new developments which combine these with MI.

Find out more about the course and how to apply.

Causal inference in epidemiology: Recent methodological developments

Course overview: Causal inference is a central aim of many empirical investigations, and arguably most studies in the fields of medicine, epidemiology and public health. However, traditionally, the role of statistics is often relegated to quantifying the extent to which chance could explain the results, whilst concerns over systematic biases due to the non-ideal nature of the data are relegated to their qualitative discussion. The field known as causal inference has changed this state of affairs, setting causal questions within a coherent framework which facilitates explicit statement of all the assumptions underlying a given analysis, in many settings developing novel, flexible analysis methods, and allowing extensive exploration of potential biases. This course will discuss the current state of the art with respect to these issues, while retaining a practical focus.

Find out more about the course and how to apply.

Training in pharmacoepidemiology and pharmacovigilance

Course overview: Develop your skills in pharmacoepidemiology, pharmacovigilance, and real-world evidence with our intensive online short courses. Learn key concepts in epidemiology, statistics, and health economics from experts in a range of sectors, including academia, regulatory, and industry.

Find out more about these courses and how to apply.