Cited by 527Statistical Methods and Bayesian InferenceStatistical Methods in EpidemiologyStatistical Methods in Clinical Trials

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Attrition in longitudinal studies

Twisk, Jos, de Vente, Wieke·2002·Journal of Clinical Epidemiology

DOI 10.1016/s0895-4356(01)00476-0

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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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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)
DOI: 10.1016/s0895-4356(01)00476-0
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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