When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts
By J. Jakobsen, C. Gluud, J. Wetterslev, P. Winkel. BMC Medical Research Methodology, 2017
2,280 citations recorded by the source corpus.
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.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/601b9d1445292bcdfd372c11b5a2eb752d7cf39e
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In Statistics and Probability
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Citations in the corpus
- The proportion of missing data should not be used to guide decisions on multiple imputation (2019, background): “These findings provide an example of valid estimates from properly specified MI at much larger proportions of missing data than current guidance [19] advises.”
- The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research* (2020, background): “It is also important to consider and justify analysis methods that account for missing data [55,56].”
- Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0 (2020, background): “It is also important to consider and justify analysis methods that account for missing data [55,56].”