LSHTM: Statistical Analysis with Missing Data Using Multiple Imputation
Date: 17th - 19th June 2025
Location: Online
Course Type: Short Course
Overview
A short course taught online by statisticians from London School of Hygiene and Tropical Medicine, and part of the School’s Centre for Data and Statistical Science for Health.
Whilst missing data frequently occurs in both observational and experimental research it can lead to a loss of statistical power, and more importantly, may introduce bias into the analysis. That's why its important to learn about how the method of multiple imputation can be used to handle missing data in statistical analyses. If you're an epidemiologist, biostatistician or other health researcher looking to learn more about statistical analysis with missing data using multiple imputation, join our short course to master this essential method.
What is the benefit of attending?
- Understand the impacts of missing data on statistical inferences and assumptions about missingness mechanisms, including missing completely at random, missing at random, and missing not at random.
- Understand the assumptions under which multiple imputations can be used to provide valid inferences from a partially observed dataset, and be able to apply it appropriately using modern statistical software.
- Understand how multiple imputations can be applied in various advanced settings, including non-linearities and interactions, missing data sensitivity analysis, propensity score analysis, and prognostic model development.
Registration
To find out more and register, please click here.