Cited by 481Statistical Methods and Bayesian InferenceStatistical Methods in EpidemiologySurvey Methodology and Nonresponse

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Imputation of missing longitudinal data: a comparison of methods

Engels, J·2003·Journal of Clinical Epidemiology

DOI 10.1016/s0895-4356(03)00170-7

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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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“Mini-mental state”
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
The Cardiovascular Health Study: design and rationale.
Fried, L. P., Borhani, N. O., Enright, P., Furberg, C. D., Gardin, J. M., Kronmal, R. A., Kuller, L. H., Manolio, T. A., Mittelmark, M. B., Newman, A., et al. (1991) · Annals of Epidemiology · doi:10.1016/1047-2797(91)90005-w · Cited by 3,652
Missing data in longitudinal studies
Laird, N. M. (1988) · Statistics in Medicine · doi:10.1002/sim.4780070131 · Cited by 713
Depression, self-derogation, substance use, and suicide ideation: Lack of purpose in life as a mediational factor
Harlow, L. L., Newcomb, M. D., & Bentler, P. M. (1986) · Journal of Clinical Psychology · doi:10.1002/1097-4679(198601)42:1 · Cited by 544
Handling missing data in survey research
Brick, J. M., & Kalton, G. (1996) · Statistical Methods in Medical Research · doi:10.1177/096228029600500302 · Cited by 529
Incomplete Data in Sample Surveys.
Titterington, D. M., Madow, W. G., Nisselson, H., Olkin, I., & Rubin, D. B. (1985) · Journal of the American Statistical Association · doi:10.2307/2288499 · Cited by 326
Compensating for missing survey data
Kalton, G. (1982) · Deep Blue (University of Michigan) · Cited by 237
Body mass index and mortality in nonsmoking older adults: the Cardiovascular Health Study.
Diehr, P. H., Bild, D. E., Harris, T. B., Duxbury, A. S., Siscovick, D. S., & Rossi, M. (1998) · American Journal of Public Health · doi:10.2105/ajph.88.4.623 · Cited by 219
Methodological Issues for Health Care Surveys.
Kasprzyk, D., Cox, B. G., & Cohen, S. B. (1986) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2069329 · Cited by 122

Show all 13 references

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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)
DOI: 10.1016/s0895-4356(03)00170-7
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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Imputation of missing longitudinal data: a comparison of methods

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