Imputation of missing longitudinal data: a comparison of methods
Abstract
Background and objectives Missing information is inevitable in longitudinal studies, and can result in biased estimates and a loss of power. One approach to this problem is to impute the missing data to yield a more complete data set. Our goal was to compare the performance of 14 methods of imputing missing data on depression, weight, cognitive functioning, and self-rated health in a longitudinal cohort of older adults.Methods We identified situations where a person had a known value following one or more missing values, and treated the known value as a "missing value." This "missing value" was imputed using each method and compared to the observed value. Methods were compared on the root mean square error, mean absolute deviation, bias, and relative variance of the estimates.Results Most imputation methods were biased toward estimating the "missing value" as too healthy, and most estimates had a variance that was too low. Imputed values based on a person's values before and after the "missing value" were superior to other methods, followed by imputations based on a person's values before the "missing value." Imputations that used no information specific to the person, such as using the sample mean, had the worst performance.Conclusions We conclude that, in longitudinal studies where the overall trend is for worse health over time and where missing data can be assumed to be primarily related to worse health, missing data in a longitudinal sequence should be imputed from the available longitudinal data for that person.
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Two trials (Hazell 2009; Ougrin 2011a) used last observation carried forward, which may introduce bias, and were rated unclear risk of bias.
“Two trials used the last observation carried forward method (Hazell 2009; Ougrin 2011a), which we understand may introduce bias (Engles 2003). We therefore rated these trials as having an unclear risk of bias for this item.”
From Interventions for self-harm in children and adolescents (2021) · cites this paper as Engles 2003
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- Authors
- Engels, J.
- Affiliations
- University of Washington (US)
- Corresponding author
- Engels, J
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2003-10
- Volume
- 56
- Issue
- 10
- Pages
- 968-976
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W2111700774
- Cited by
- 478
- References
- 13
- Citations by year
- 2026: 14 · 2025: 27 · 2024: 26 · 2023: 26 · 2022: 22 · 2021: 35 · 2020: 16 · 2019: 19 · 2018: 29 · 2017: 23 · 2016: 19 · 2015: 23 · 2014: 23 · 2013: 22 · 2012: 13
- FWCI
- 14.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (n01hc085079-024, n01hc085086-022)
- National Heart, Lung, and Blood Institute (N01 HC85086, N01-HC85079)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- missing data imputation, longitudinal data, older adults, depression, weight, cognitive function, self-rated health, imputation method comparison
- MeSH terms
- Aged, Analysis of Variance, Coronary Disease, Data Interpretation, Statistical, Depression, Female, Health Status, Humans, Longitudinal Studies, Male, Research Design, Risk Factors, United States, Bias, Stroke
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