A short course taught in London by statisticians from LSHTM, and part of the School’s Centre for Data and Statistical Science for Health.
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 overall aim of this course is for participants to learn about how the method of multiple imputation can be used to handle missing data in statistical analyses and to understand the assumptions under which this is valid. In addition to introducing the method in more standard settings, we will explore its use in a range of more advanced situations, including in the presence of non-linearities and interactions, propensity score analysis, prognostic model development, and for performing sensitivity analyses.
Who should apply?
Epidemiologists, biostatisticians and other health researchers with strong quantitative skills and experience in statistical analysis. In particular, we will expect familiarity with regression models, such as linear and logistic regression, and interpretation of their results. Computer practicals will use the statistical software package R, and so participants should be familiar with using R for performing statistical analyses. Full R code solutions will be provided.
Teaching format
The course will be taught in London at the London School of Hygiene and Tropical Medicine across 3 days. Teaching will take place through a blend of lectures and hands-on, computer practical sessions with an anticipated 1 hour of lecture followed by a 1.5 hours computer practical both in the morning and the afternoon on each day. The course will take place from approximately 9:30am to 5.00pm British Summer Time (BST).
Students on the course will be required to bring their own laptop with R installed. Please see further guidance on the installation of R in your course acceptance information.
Course Content
The course will cover:
- The effects of missing data on statistical inferences
- Missingness mechanism assumptions, including missing completely at random, missing at random, missing not at random.
- Multiple imputation for missing data, based on joint models and fully conditional specification approaches, and Rubin's pooling rules.
- Multiple imputation accommodating non-linearities and interactions.
- Multiple imputation for sensitivity analysis.
- Multiple imputation in the context of propensity score analysis.
- Multiple imputation in the context of prognostic model development and deployment.
Through computer practicals using R, participants will learn how to apply the statistical methods introduced in the course to realistic datasets.
Course Certificate and Assessment
There will be no formal assessment, but participants will receive a Certificate of Attendance.
Applying for this course
Applications for 2024 are now open and can be made via our online application form.
Please read LSHTM's Admissions policies prior to submitting your application.
Visas
The student is responsible for obtaining any visa or other permissions to attend the course, and is encouraged to start the application process as early as possible as obtaining a visa for the UK can sometimes take a long time. The Short Courses team can provide supporting documentation if requested.
Accommodation
A list of hotels located in the vicinity of LSHTM, along with further resources for short term accommodation, can be found on our accommodation pages.
Important information
Please note:
- Students will be required to bring their own laptops. The Stata package will be available for the duration of the course.
- If you have been offered a place on the course you will not be able to register without bringing a formal ID (Passport) and without having obtained the correct visa if required.
- It is essential that you read the current visa requirements for short course students.
- LSHTM may cancel courses two weeks before the first day of the course if numbers prove insufficient. In those circumstances, course fees will be refunded.
- LSHTM cannot accept responsibility for accommodation, travel and other losses incurred as a result of the course being cancelled.