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

  • N/A
    Application Deadline
  • 3 days
    Duration
  • Tuition
    735
    Tuition (Module)
    735
    Tuition (Module)
  • English (take IELTS)
    Language
,
Missing data frequently occur 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.
  • Overview
  • Programme outline
  • Key facts
  • Admission requirements
  • Fees and funding

About

A short course taught in London by statisticians from the Department of Medical Statistics, and part of the LSHTM Centre for Statistical Methodology.

Overview

Missing data frequently occur 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.

Detailed Programme Facts

  • Full-time duration 3 days
  • Languages
    • English
  • Delivery mode
    On Campus
  • Entry Level
    Master

Programme Structure

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.

Course Certificate and Assessment

There will be no formal assessment but a Certificate of Attendance will be awarded.

Lecturers

Dr Jonathan Bartlett, Dr James Carpenter and Professor Mike Kenward

Academic Requirements

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.

Tuition Fee Per Module

  • GBP 735 International
  • GBP 735 EU/EEA

*Payable by: 11 May

Funding

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