Multiple imputation in a longitudinal cohort study: a case study of sensitivity to imputation methods

Romaniuk, Helena, Patton, George C and Carlin, John B 2014, Multiple imputation in a longitudinal cohort study: a case study of sensitivity to imputation methods, American journal of epidemiology, vol. 180, no. 9, pp. 920-932, doi: 10.1093/aje/kwu224.

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Title Multiple imputation in a longitudinal cohort study: a case study of sensitivity to imputation methods
Author(s) Romaniuk, Helena
Patton, George C
Carlin, John B
Journal name American journal of epidemiology
Volume number 180
Issue number 9
Start page 920
End page 932
Total pages 13
Publisher Oxford University Press
Place of publication Oxford, Eng.
Publication date 2014-11-01
ISSN 0002-9262
1476-6256
Keyword(s) longitudinal cohort study
missing data
multiple imputation
sensitivity analysis
amphetamine-related disorders
logistic models
longitudinal studies
marijuana smoking
probability
Victoria
epidemiology
science & technology
life sciences & biomedicine
public, environmental & occupational health
Summary Multiple imputation has entered mainstream practice for the analysis of incomplete data. We have used it extensively in a large Australian longitudinal cohort study, the Victorian Adolescent Health Cohort Study (1992-2008). Although we have endeavored to follow best practices, there is little published advice on this, and we have not previously examined the extent to which variations in our approach might lead to different results. Here, we examined sensitivity of analytical results to imputation decisions, investigating choice of imputation method, inclusion of auxiliary variables, omission of cases with excessive missing data, and approaches for imputing highly skewed continuous distributions that are analyzed as dichotomous variables. Overall, we found that decisions made about imputation approach had a discernible but rarely dramatic impact for some types of estimates. For model-based estimates of association, the choice of imputation method and decisions made to build the imputation model had little effect on results, whereas estimates of overall prevalence and prevalence stratified by subgroup were more sensitive to imputation method and settings. Multiple imputation by chained equations gave more plausible results than multivariate normal imputation for prevalence estimates but appeared to be more susceptible to numerical instability related to a highly skewed variable.
Language eng
DOI 10.1093/aje/kwu224
Field of Research 11 Medical And Health Sciences
01 Mathematical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2014, The Author
Persistent URL http://hdl.handle.net/10536/DRO/DU:30113851

Document type: Journal Article
Collection: PVC's Office - Health
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