PSI Training Course: Missing Data
Dates: Mon.9th, Tues.10th, Thurs.12th & Fri.13th October 2023
Time: 09:00-12:00 BST (each day)
Speakers: Jonathan Bartlett and James Carpenter (LSHTM)
Who is this event intended for? This course is intended for clinical trial statisticians who are interested in learning more about statistical methods for handling missing data in clinical trial analyses.
What is the benefit of attending? By the end of the course participants will be familiar with the key concepts (e.g. missing at random) and statistical methods (e.g. multiple imputation) relevant when estimating treatment effects in trials where some data are missing.
Early Bird PSI Members = £320+VAT
Early Bird Non-Members = £430*+VAT
*Please note: Early Bird prices expire at 17:00 on Friday 8th September.
Standard PSI Members = £360+VAT
Standard Non-Members = £470*+VAT
*Please note: Non-Member rates include PSI membership until 31 Dec. 2024.
To book your place, please click here.
This course will introduce participants to the key concepts and methods relevant for analysing clinical trials when some data are missing. We will describe missing data assumptions and Rubin’s framework for classifying them, based on missing completely at random, missing at random (MAR), and missing not at random, and what these imply when missingness is due to dropout or the occurrence of intercurrent events. We will describe the use of mixed models and multiple imputation to handle missingness under MAR, and finally discuss methods for conducting missing data sensitivity analyses, including reference based imputation methods.
The course will cover:
- Introduction to estimands and missing data in trials; review of missing data assumptions & terminology (e.g. missing at random)
- Performing analyses under missing at random for continuous outcome data, using mixed models and multiple imputation (including consideration of retrieved dropout multiple imputation)
- Performing analyses under missing at random for binary data, using full conditional specification for multiple imputation with a GEE analysis model
- Sensitivity analyses using multiple imputation, including reference based imputation methods
Please note: Each of the above will be presented in a one hour lecture, followed by a two hour interactive computer practical. Computer practicals will be taught using R and so having R or R Studio installed on your personal laptop/computer is required to participate in the practicals.
Jonathan Bartlett is a Professor in Medical Statistics at the London School of Hygiene & Tropical Medicine.
His research interests are focused around missing data and causal inference methods, and more recently, how these can be applied to target different estimands in clinical trials. He has held previous positions at AstraZeneca and the University of Bath, and maintains a blog thestatsgeek.com
James Carpenter is Professor of Medical Statistics at the London School of Hygiene & Tropical Medicine, and MRC Investigator in trials methodology at the MRC CTU at UCL.
His principal research interests are coping with missing data in clinical trials and complex hierarchical models, estimands, sensitivity analysis, meta-analysis and novel trial designs.