Multiple imputation in clinical trials – what difference does it make?: A systematic review of the impact of multiple imputation on treatment effect estimation

Multiple imputation did not consistently lead to more precise treatment effect estimates than complete case analysis in the trials included in the review. Findings should not be construed as an argument against the use of multiple imputation but suggest the standard approach of inflating the sample size for anticipated loss to follow-up is reasonable when multiple imputation is planned for analysis.

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Publication Details

Journal
Clinical Trials
Published
2026-03-03
DOI
https://doi.org/10.1177/17407745261422359
Primary Topic
Statistical Methods and Bayesian Inference
Type
article
Field-Weighted Citation Impact
0.00
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article

Multiple imputation in clinical trials – what difference does it make?: A systematic review of the impact of multiple imputation on treatment effect estimation

Jana M Bednarz, Katherine J Lee, Lisa N Yelland, Thomas R Sullivan
Clinical Trials
Statistical Methods and Bayesian Inference
article

Multiple imputation in clinical trials – what difference does it make?: A systematic review of the impact of multiple imputation on treatment effect estimation

Jana M Bednarz, Katherine J Lee, Lisa N Yelland, Thomas R Sullivan
article en

Abstract

Multiple imputation did not consistently lead to more precise treatment effect estimates than complete case analysis in the trials included in the review. Findings should not be construed as an argument against the use of multiple imputation but suggest the standard approach of inflating the sample size for anticipated loss to follow-up is reasonable when multiple imputation is planned for analysis.

Clinical Trials
The University of Melbourne (AU), South Australian Health and Medical Research Institute (AU), Murdoch Children's Research Institute (AU), The University of Adelaide (AU)
Good health and well-being
Openalex Percentile: Top 38%
Statistical Methods and Bayesian Inference
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