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Using multiple imputation to deal with missing data and attrition in longitudinal studies with repeated measures of patient-reported outcomes

Authors Biering K, Hjollund NH, Frydenberg M

Received 4 August 2014

Accepted for publication 18 November 2014

Published 16 January 2015 Volume 2015:7 Pages 91—106

DOI https://doi.org/10.2147/CLEP.S72247

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 4

Editor who approved publication: Professor Vera Ehrenstein


Karin Biering,1 Niels Henrik Hjollund,2,3 Morten Frydenberg4

1Danish Ramazzini Centre, Department of Occupational Medicine – University Research Clinic, Hospital West Jutland, Herning, Denmark; 2WestChronic, Regional Hospital West Jutland, Herning, Denmark; 3Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; 4Section of Biostatistics, Department of Public Health, Aarhus University, Aarhus, Denmark

Objective: Missing data is a ubiquitous problem in studies using patient-reported measures, decreasing sample sizes and causing possible bias. In longitudinal studies, special problems relate to attrition and death during follow-up. We describe a methodological approach for the use of multiple imputation (MI) to meet these challenges.
Methods: In a cohort of patients treated with percutaneous coronary intervention followed with use of repetitive questionnaires and information from national registers over 3 years, only 417 out of 1,726 patients had complete data on all measure points and covariates. We suggest strategies for use of MI and different methods for dealing with death along with sensitivity analysis of deviations from the assumption of missing at random, all with the use of standard statistical software. The Mental Component Summary from Short Form 12-item survey was used as an example.
Conclusion: Ignoring missing data may cause bias of unknown size and direction in longitudinal studies. We have illustrated that MI is a feasible method to try to deal with bias due to missing data in longitudinal studies, including attrition and nonresponse, and should be considered in combination with analysis of sensitivity in longitudinal studies. How to handle dropout due to death is still open for debate.

Keywords: PCI, SF-12, nonparticipants, nonrespondents

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