When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts
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TLDR
This work considers how to optimise the handling of missing data during the planning stage of a randomised clinical trial and recommends analytical approaches which may prevent bias caused by unavoidable missing data.Abstract:
Missing 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. The 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. Handling 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. We present a practical guide and flowcharts describing when and how multiple imputation should be used to handle missing data in randomised clinical.read more
Citations
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The proportion of missing data should not be used to guide decisions on multiple imputation.
TL;DR: Evidence is provided that for MAR data, valid MI reduces bias even when the proportion of missingness is large, and researchers are advised to use FMI to guide choice of auxiliary variables for efficiency gain in imputation analyses, and that sensitivity analyses including different imputation models may be needed if the number of complete cases is small.
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References
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Cochrane Handbook for Systematic Reviews of Interventions, Version 5.1.0. The Cochrane Collaboration
Journal ArticleDOI
Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls.
Jonathan A C Sterne,Ian R. White,John B. Carlin,Michael Spratt,Patrick Royston,Michael G. Kenward,Angela M. Wood,James R. Carpenter +7 more
TL;DR: The appropriate use and reporting of the multiple imputation approach to dealing with missing data is described by Jonathan Sterne and colleagues.
Journal ArticleDOI
The Prevention and Treatment of Missing Data in Clinical Trials
Roderick J. A. Little,Ralph B. D'Agostino,Michael L. Cohen,Kay Dickersin,Scott S. Emerson,John T. Farrar,Constantine Frangakis,Joseph W. Hogan,Geert Molenberghs,Susan A. Murphy,James D. Neaton,Andrea Rotnitzky,Daniel O. Scharfstein,Weichung Joe Shih,Jay P. Siegel,Hal S. Stern +15 more
TL;DR: Methods for preventing missing data and, failing that, dealing with data that are missing in clinical trials are reviewed.
Journal ArticleDOI
Hydroxyethyl Starch 130/0.42 versus Ringer's Acetate in Severe Sepsis
Anders Perner,Nicolai Haase,Anne Berit Guttormsen,Anne Berit Guttormsen,Jyrki Tenhunen,Gudmundur Klemenzson,Anders Aneman,Kristian Rørbæk Madsen,Morten Hylander Møller,Jeanie M. Elkjær,Lone Musaeus Poulsen,Asger Bendtsen,Robert Winding,Morten Steensen,Pawel Berezowicz,Peter Søe-Jensen,Morten H. Bestle,Kristian Strand,Jørgen Wiis,Jonathan White,Klaus J. Thornberg,Lars Quist,Jonas B. Nielsen,Lasse H. Andersen,Lars Broksø Holst,Katrin Thormar,Anne Lene Kjældgaard,Maria Louise Fabritius,Frederik Mondrup,Frank Christian Pott,Thea Palsgaard Møller,Per Winkel,Jørn Wetterslev +32 more
TL;DR: Patients with severe sepsis assigned to fluid resuscitation with HES 130/0.42 had an increased risk of death at day 90 and were more likely to require renal-replacement therapy, as compared with those receiving Ringer's acetate.
Journal ArticleDOI
Industry sponsorship and research outcome
TL;DR: The analyses suggest the existence of an industry bias that cannot be explained by standard 'Risk of bias' assessments.