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. 2015 Jan 16:7:91-106.
doi: 10.2147/CLEP.S72247. eCollection 2015.

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

Karin Biering  1 , Niels Henrik Hjollund  2 , Morten Frydenberg  3
Affiliations

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

Karin Biering et al. Clin Epidemiol. .

Abstract

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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Figures

Figure 1
Figure 1
Exemplified overview of some respondent types in a follow up study of PCI patients.
Figure 2
Figure 2
Samples of nine patients observed data (A) and three different patients observed and imputed data (B–D). Abbreviations: MCS, Mental Component Summary; PCI, percutaneous coronary intervention.
Figure 3
Figure 3
Mean scores and mean changes of Mental Component Summary with observed data, variations of multiple imputation approaches related to dead (A and B), and sensitivity analyses (C and D). Abbreviations: MCS, Mental Component Summary; CI, confidence interval; PCI, percutaneous coronary intervention.
Figure 4
Figure 4
Sex-stratified unadjusted MCS mean scores: observed and imputation A (A); adjusted differences in MCS: observed, imputation A, and sensitivity analysis (B). Abbreviations: MCS, Mental Component Summary; CI, confidence interval; PCI, percutaneous coronary intervention.
See this image and copyright information in PMC

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