The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Attrition in longitudinal studies
Abstract
The purpose of this paper was to illustrate the influence of missing data on the results of longitudinal statistical analyses [i.e., MANOVA for repeated measurements and Generalised Estimating Equations (GEE)] and to illustrate the influence of using different imputation methods to replace missing data. Besides a complete dataset, four incomplete datasets were considered: two datasets with 10% missing data and two datasets with 25% missing data. In both situations missingness was considered independent and dependent on observed data. Imputation methods were divided into cross-sectional methods (i.e., mean of series, hot deck, and cross-sectional regression) and longitudinal methods (i.e., last value carried forward, longitudinal interpolation, and longitudinal regression). Besides these, also the multiple imputation method was applied and discussed. The analyses were performed on a particular (observational) longitudinal dataset, with particular missing data patterns and imputation methods. The results of this illustration shows that when MANOVA for repeated measurements is used, imputation methods are highly recommendable (because MANOVA as implemented in the software used, uses listwise deletion of cases with a missing value). Applying GEE analysis, imputation methods were not necessary. When imputation methods were used, longitudinal imputation methods were often preferable above cross-sectional imputation methods, in a way that the point estimates and standard errors were closer to the estimates derived from the complete dataset. Furthermore, this study showed that the theoretically more valid multiple imputation method did not lead to different point estimates than the more simple (longitudinal) imputation methods. However, the estimated standard errors appeared to be theoretically more adequate, because they reflect the uncertainty in estimation caused by missing values.
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In the citing paper
“GEE allows for not using imputation methods for the missing data, because the participants with missing data are not excluded from the analysis [46].”
From Fibrates as drugs with senolytic and autophagic activity for osteoarthritis therapy (2019) · cites this paper as [46]
In the citing paper
“We chose GEE models because EMA data have a hierarchical structure; this type of model adjusts for dependency of repeated measures within one participant and can handle non-normally distributed data. GEE models are also suitable for dealing with missing data; it is not required to exclude participants with missing questionnaires, nor should missing questionnaires be imputed beforehand.”
From Experienced burden of and adherence to smartphone-based ecological momentary assessment in persons with affective disorders (2020) · cites this paper as [29]
Citations
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Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012
Analysis of Incomplete Multivariate Data
Booth, D. E., & Schafer, J. L. (2000) · Technometrics · doi:10.2307/1271466 · Cited by 5,627
Multiple imputation after 18+ years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476908 · Cited by 4,072
Multiple Imputation After 18+ Years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.2307/2291635 · Cited by 2,686
Multiple Imputation for Nonresponse in Surveys.
Lynn, P. J., & Rubin, D. B. (1988) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348774 · Cited by 2,330
Statistical Analysis with Missing Data.
Chatfield, C., Little, R. J. A., & Rubin, D. B. (1988) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982783 · Cited by 2,203
Cited by
Sorted by citations
Multiple imputation of discrete and continuous data by fully conditional specification
van Buuren, S. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074463 · Cited by 2,827
A Review of Hot Deck Imputation for Survey Non‐response
Andridge, R., & Little, R. J. A. (2010) · International Statistical Review · doi:10.1111/j.1751-5823.2010.00103.x · Cited by 1,074
Loss to follow-up in cohort studies: how much is too much?
Kristman, V., Manno, M., & Côté, P. (2004) · European Journal of Epidemiology · doi:10.1023/b:ejep.0000036568.02655.f8 · Cited by 740
“Under this assumption, the probability of being lost to follow-up depends on the outcome to be measured and cannot be completely explained by the covariates [12].”
Influentialvan der Heijden, G. V. D., Donders, A., Stijnen, T., & Moons, K. (2006) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2006.01.015 · Cited by 664
Wood, A., White, I. R., & Thompson, S. G. (2004) · Clinical Trials · doi:10.1191/1740774504cn032oa · Cited by 559
“There is a large literature comparing methods to handle missing outcomes [7-10] and developing complex statistical procedures for handling MNAR situations and performing sensitivity analyses”
Background
Metadata
- Authors
- Twisk, J., & de Vente, W.
- Affiliations
- Vrije Universiteit Amsterdam (NL)
- Corresponding author
- Twisk, Jos
- ORCID
- Twisk, Jos
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2002-04
- Volume
- 55
- Issue
- 4
- Pages
- 329-337
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W2032594557
- Cited by
- 527
- References
- 18
- Citations by year
- 2026: 18 · 2025: 15 · 2024: 9 · 2023: 20 · 2022: 24 · 2021: 43 · 2020: 18 · 2019: 36 · 2018: 28 · 2017: 27 · 2016: 26 · 2015: 23 · 2014: 29 · 2013: 28 · 2012: 27
- FWCI
- 14.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- attrition, missing data, longitudinal study, imputation methods, multiple imputation, generalized estimating equations, listwise deletion, hot deck imputation, cross-sectional regression, standard errors
- MeSH terms
- Analysis of Variance, Data Interpretation, Statistical, Epidemiologic Methods, Humans, Longitudinal Studies, Mathematical Computing, Research Design
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