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Missing Data: The Importance and Impact of Missing Data from Clinical Research

Published online by Cambridge University Press:  23 April 2014

Christine R. Padgett*
Affiliation:
School of Psychology, University of Tasmania, Australia
Clive E. Skilbeck
Affiliation:
School of Psychology, University of Tasmania, Australia Tasmanian Neurotrauma Register, Royal Hobart Hospital, Tasmania, Australia
Mathew James Summers
Affiliation:
School of Psychology, University of Tasmania, Australia Wicking Dementia Research & Education Centre, University of Tasmania, Hobart, Australia
*
Address for correspondence: Christine Padgett, School of Psychology, University of Tasmania, Australia. E-mail: Christine.Padgett@utas.edu.au
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Abstract

There is compelling evidence that traditional methods used to address the detrimental impacts of missing data are inadequate. Despite this, researchers have been slow to utilise newer statistical approaches known to be more effective. The aim of the current article is to offer a conceptual explanation of the rationale for using newer missing data techniques, with a focus on multiple imputation (MI). To illustrate the relative efficacy of deletion, single imputation and multiple imputation techniques in the clinical setting, 20 cases were selected randomly from a population study investigating the cognitive sequelae of traumatic brain injury (TBI), and 8 out of 20 cases had scores on one variable deleted to simulate a missing data set. Comparing the parameter estimates obtained by each technique to the known parameters of the complete data set revealed that MI outperformed deletion and single imputation approaches. It is therefore recommended that more sophisticated techniques such as MI should be considered in clinical research.

Type
Articles
Copyright
Copyright © Australasian Society for the Study of Brain Impairment 2014 

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