ArticleBMC medical research methodology2017
When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts.
Article in BMC medical research methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 10 registered trials, which are not on this map. Cited by 1,322 papers, 7 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1,322 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Pooled it
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- The Locus-of-Hope Enhancement Program for Filipino Community Health Workers' Hope and Well-Being: A Parallel Randomized Alternative-Treatments Study.Journal of community psychology · 2026Trial
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- Exploring fatigue in clinically depressed adolescents: a secondary analysis of the IMPACT study sample.BMC psychology · 2026Trial
1,262 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMissing data may seriously compromise inferences from randomised clinical trials, especially if missing data are not handled appropriately. The potential bias due to missing data depends on the mechanism causing the data to be missing, and the analytical methods applied to amend the missingness. Therefore, the analysis of trial data with missing values requires careful planning and attention.
methodsThe authors had several meetings and discussions considering optimal ways of handling missing data to minimise the bias potential. We also searched PubMed (key words: missing data; randomi*; statistical analysis) and reference lists of known studies for papers (theoretical papers; empirical studies; simulation studies; etc.) on how to deal with missing data when analysing randomised clinical trials.
resultsHandling missing data is an important, yet difficult and complex task when analysing results of randomised clinical trials. We consider how to optimise the handling of missing data during the planning stage of a randomised clinical trial and recommend analytical approaches which may prevent bias caused by unavoidable missing data. We consider the strengths and limitations of using of best-worst and worst-best sensitivity analyses, multiple imputation, and full information maximum likelihood. We also present practical flowcharts on how to deal with missing data and an overview of the steps that always need to be considered during the analysis stage of a trial.
conclusionsWe present a practical guide and flowcharts describing when and how multiple imputation should be used to handle missing data in randomised clinical.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.