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

• Published in: Clinical epidemiology Vol. 7; no. default; pp. 91 - 106
• Main Authors: Biering, Karin, Hjollund, Niels Henrik, Frydenberg, Morten
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 01.01.2015; Taylor & Francis Ltd; Dove Medical Press
• Subjects: Analysis; Bias; Cardiac patients; Clinical medicine; Clinical outcomes; Comorbidity; Confidence intervals; Datasets; Epidemiology; Estimates; Health aspects; Hospitals; Longitudinal studies; Medical records; Methodology; Missing data; Participation; Patient outcomes; Patients; Public health; Quality of life; Sensitivity analysis; Transluminal angioplasty; Variables; Within-subjects design; Denmark; Jutland; Aarhus Denmark; SF-12; nonparticipants; nonrespondents; PCI
• ISSN: 1179-1349; 1179-1349
• DOI: 10.2147/CLEP.S72247

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. 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. 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.