Differential dropout and bias in randomised controlled trials: when it matters and when it may notBMJ 2013; 346 doi: https://doi.org/10.1136/bmj.e8668 (Published 21 January 2013) Cite this as: BMJ 2013;346:e8668
- Melanie L Bell, senior research fellow1,
- Michael G Kenward, professor2,
- Diane L Fairclough, professor3,
- Nicholas J Horton, professor4
- 1Psycho-Oncology Co-operative Research Group (PoCoG), University of Sydney, Sydney Australia
- 2Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
- 3Department of Preventive Medicine and Biometry, University of Colorado at Denver, Denver, CO, USA
- 4Department of Mathematics and Statistics, Smith College, Northampton, MA, USA
- Correspondence to: M L Bell
- Accepted 27 November 2012
Dropout in longitudinal randomised controlled trials is common and a potential source of bias in terms of evidence based medicine. A review of 71 randomised controlled trials in four top medical journals showed dropout rates of 20% or more in 18% of the trials.1 Similar rates were found in a review of quality of life outcomes.2 In specialist journals, the rates are likely to be higher.
When dropout rates differ between treatment arms, so that fewer patients are followed up in one arm than the other, it is sometimes called “differential dropout” or “differential attrition.” Despite extensive literature on incomplete data methods for randomised controlled trials, guidance from the CONSORT reports, and the National Research Council’s recent report on missing data,3 many applied researchers have misconceptions about how to handle dropout.1 2 4 Two common misunderstandings about differential dropout need to be debunked:
Myth 1—if dropout rates in longitudinal clinical trials are similar between study arms, bias is not a concern.
Myth 2—if dropout rates are dissimilar between study arms, the results will necessarily be biased.
Although differential dropout can bias results, equal dropout rates between …