Workshop: Handling missing outcome data in randomised trials
- Venue: MRC Biostatistics Unit, Cambridge
- Date: June 20, 2011
- Participants: 37
Aims:
- Review some popular ways to analyse trials with missing data, focussing on the assumptions underlying them
- Discuss what assumptions may be plausible and how one should decide this in particular trials
- Focus on mixed models: describe the theory, how they should be implemented, what are the pitfalls
- Demonstrate how to analyse data using mixed models and multiple imputation in Stata and SAS
- Discuss the advantages and disadvantages of the two methods
- Discuss the different issues involved in handling missing baselines
Agenda
- Lecture (1hr)
Introduction to missing data: assumptions (MCAR/MAR/MNAR and others); ITT principle; common methods (CC, LOCF); handling missing baselines
- Lecture (0.5hr)
Mixed models
- Practical (1hr)
Mixed models in Stata / SAS / R
- Lecture (0.5hr)
Multiple imputation
- Lecture (0.5hr)
Sensitivity analysis
- Practical (1.5hr)
Multiple imputation/Sensitivity analysis in Stata / SAS / R
- Discussion (0.5hr)
Which methods work best in practice? Which should we use?
Outputs
To view course materials, including lecture notes, practicals, data and programs in SAS, click here.
