International journal of epidemiology
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Comparative Study
Comparison of imputation and modelling methods in the analysis of a physical activity trial with missing outcomes.
Longitudinal 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. ⋯ Results 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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Trial investigators frequently exclude patients from trial analyses which may bias estimates of the effect of treatment. Combining these estimates in a meta-analysis could aggregate any such biases. ⋯ Trials, systematic reviews, and meta-analyses may be prone to bias associated with post-randomization exclusion of patients. Wherever possible, the level of such exclusions should be taken into account when assessing the potential for bias in trials, systematic reviews, and meta-analyses. Ideally, trials, systematic reviews, and meta-analyses should be based on all randomized patients.