Statistical Analysis with Missing Data Using Multiple Imputation
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.
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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.
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Infectious Disease 'Omics
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.
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