Statistical primer: how to deal with missing data in scientific research?

Interactive Cardiovascular and Thoracic Surgery
Grigorios PapageorgiouMostafa M Mokhles

Abstract

Missing data are a common challenge encountered in research which can compromise the results of statistical inference when not handled appropriately. This paper aims to introduce basic concepts of missing data to a non-statistical audience, list and compare some of the most popular approaches for handling missing data in practice and provide guidelines and recommendations for dealing with and reporting missing data in scientific research. Complete case analysis and single imputation are simple approaches for handling missing data and are popular in practice, however, in most cases they are not guaranteed to provide valid inferences. Multiple imputation is a robust and general alternative which is appropriate for data missing at random, surpassing the disadvantages of the simpler approaches, but should always be conducted with care. The aforementioned approaches are illustrated and compared in an example application using Cox regression.

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Citations

Jul 17, 2018·Interactive Cardiovascular and Thoracic Surgery·Graeme L HickeyFriedhelm Beyersdorf
Mar 26, 2019·Journal of American College Health : J of ACH·Erin Vinoski Thomas, Jan Warren-Findlow
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May 1, 2020·The Journal of Nursing Education·Dawn M Aycock, Matthew J Hayat
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Sep 17, 2021·Plastic and Reconstructive Surgery·Denis Souto ValenteChristopher J Pannucci
May 4, 2020··Nugraha Priya UtamaHeru Nugroho

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