• Angela M Wood, Ian R White, Melvyn Hillsdon, and James Carpenter.
• MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 2SR, UK. angela.wood@mrc-bsu.cam.ac.uk
• Int J Epidemiol. 2005 Feb 1;34(1):89-99.

BackgroundLongitudinal studies almost always have some individuals with missing outcomes. Inappropriate handling of the missing data in the analysis can result in misleading conclusions. Here we review a wide range of methods to handle missing outcomes in single and repeated measures data and discuss which methods are most appropriate.MethodsUsing data from a randomized controlled trial to compare two interventions for increasing physical activity, we compare complete-case analysis; ad hoc imputation techniques such as last observation carried forward and worst-case; model-based imputation; longitudinal models with random effects; and recently proposed joint models for repeated measures data and non-ignorable dropout.ResultsEstimated intervention effects from ad hoc imputation methods vary widely. Standard multiple imputation and longitudinal modelling agree closely, as they should. Modifying the modelling method to allow for non-ignorable dropout had little effect on estimated intervention effects, but imputing using a common imputation model in both groups gave more conservative results.ConclusionsResults from ad hoc imputation methods should be avoided in favour of methods with more plausible assumptions although they may be computationally more complex. Although standard multiple imputation methods and longitudinal modelling methods are equivalent for estimating the treatment effect, the two approaches suggest different ways of relaxing the assumptions, and the choice between them depends on contextual knowledge.

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