Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/122008
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Type: Journal article
Title: Should multiple imputation be the method of choice for handling missing data in randomized trials?
Author: Sullivan, T.
White, I.
Salter, A.
Ryan, P.
Lee, K.
Citation: Statistical Methods in Medical Research, 2018; 27(9):2610-2626
Publisher: SAGE Journals
Issue Date: 2018
ISSN: 0962-2802
1477-0334
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Responsibility: 
Thomas R Sullivan, Ian R White, Amy B Salter, Philip Ryan, Katherine J Lee
Abstract: The use of multiple imputation has increased markedly in recent years, and journal reviewers may expect to see multiple imputation used to handle missing data. However in randomized trials, where treatment group is always observed and independent of baseline covariates, other approaches may be preferable. Using data simulation we evaluated multiple imputation, performed both overall and separately by randomized group, across a range of commonly encountered scenarios. We considered both missing outcome and missing baseline data, with missing outcome data induced under missing at random mechanisms. Provided the analysis model was correctly specified, multiple imputation produced unbiased treatment effect estimates, but alternative unbiased approaches were often more efficient. When the analysis model overlooked an interaction effect involving randomized group, multiple imputation produced biased estimates of the average treatment effect when applied to missing outcome data, unless imputation was performed separately by randomized group. Based on these results, we conclude that multiple imputation should not be seen as the only acceptable way to handle missing data in randomized trials. In settings where multiple imputation is adopted, we recommend that imputation is carried out separately by randomized group.
Keywords: clinical trials
intention to treat
linear mixed model
missing data
multiple imputation
Rights: © The Author(s) 2016. Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0).
DOI: 10.1177/0962280216683570
Grant ID: http://purl.org/au-research/grants/nhmrc/1053609
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