Statistical Analysis with Missing Data using Multiple Imputation and Inverse Probability Weighting
Short course
In London
Description
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Type
Short course
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Level
Intermediate
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Location
London
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Duration
Flexible
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Start date
Different dates available
A short course taught in London by statisticians from the Department of Medical Statistics, and part of the School's Centre for Statistical Methodology.
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.
During the course participants will receive a copy of the recently published book "Multiple imputation and its application" by Carpenter and Kenward.
Facilities
Location
Start date
Start date
About this course
Epidemiologists, biostatisticians and other health researchers with strong quantitative skills and experience in statistical analysis. Stata will be used for the computer practicals, and so familiarity with the package is highly desirable, although full Stata code and solutions will be provided.
Participants will need to bring a laptop if they wish to access materials in real time as all lecture material will only be made available electronically.
Reviews
Subjects
- Probability
- Content
- Content Insurance
- Random
- Statistical
- Introduction
- Methods
- Weighting
- Contrast
- Assessment
Course programme
The course will:
- provide an introduction to the issues raised by missing data, and the associated statistical jargon (missing completely at random, missing at random, missing not at random)
- illustrate the shortcomings of ad-hoc methods for 'handling' missing data
- introduce multiple imputation for statistical analysis with missing data
- compare and contrast this with other methods, in particular inverse probability weighting and doubly robust methods, and
- to introduce accessible methods for exploring the sensitivity of inference to the missing at random assumption
- Through computer practicals using Stata, participants will learn how to apply the statistical methods introduced in the course to realistic datasets.
There will be no formal assessment, but participants will receive a Certificate of Attendance.
Statistical Analysis with Missing Data using Multiple Imputation and Inverse Probability Weighting