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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Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
Applied Regression Analysis and Other Multivariable Methods
Walker, E., Kleinbaum, D. G., Kupper, L. L., & Muller, K. E. (1989) · Technometrics · doi:10.2307/1270375 · Cited by 8,538
Longitudinal Data Analysis for Discrete and Continuous Outcomes
Zeger, S. L., & Liang, K. (1986) · Biometrics · doi:10.2307/2531248 · Cited by 7,781
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

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Attrition in longitudinal population studies: Does it affect the generalizability of the findings?1An introduction to the series
Deeg, D. J. H. (2002) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(01)00472-3 · Cited by 97
Effects of short-term exercise training on signs and symptoms of generalized anxiety disorder
Herring, M. P., Jacob, M. L., Suveg, C., & O’Connor, P. J. (2011) · Mental Health and Physical Activity · doi:10.1016/j.mhpa.2011.07.002 · Cited by 93
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Schistosoma japonicum Reinfection after Praziquantel Treatment Causes Anemia Associated with Inflammation
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“These results were considered to be false-negative results (falling under the limit of detection for stool examination), and egg counts were imputed using the “last value carried over” method (31).”
Method
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Ahern, K., & Brocque, R. M. L. (2004) · Field Methods · doi:10.1177/1525822x04271006 · Cited by 93
Quality of Life of Women After a First Diagnosis of Breast Cancer Using a Self-Management Support mHealth App in Taiwan: Randomized Controlled Trial
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“GEE analysis showed a statistically significant difference ( P =.03) in the interaction between groups and time, which meant the experimental group had a greater mean summary score for the QLQ-C30 (difference=5.05) than the control group after the intervention at the 3-month assessment (see Table 3). e Diff: differenc…”
MethodInfluential
Efficacy of Two Tailored Interventions Promoting Physical Activity in Older Adults
van Stralen, M. M., de Vries, H., Mudde, A. N., Bolman, C. A. W., & Lechner, L. (2009) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2009.07.009 · Cited by 91
Predictors identified for losses to follow-up among HIV-seropositive patients
Lanoy, É., Mary‐Krause, M., Tattevin, P., Dray‐Spira, R., Duvivier, C., FISCHER, P., Obadia, Y., Lert, F., & Costagliola, D. G. (2006) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2005.11.024 · Cited by 91
Long-term efficacy of a printed or a Web-based tailored physical activity intervention among older adults
Peels, D. A., Bolman, C. A. W., Golsteijn, R. H. J., de Vries, H., Mudde, A. N., van Stralen, M. M., & Lechner, L. (2013) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/1479-5868-10-104 · Cited by 89
“Applying multilevel analyses to an incomplete dataset has been shown to give more accurate estimations than applying imputation methods [17].”
BackgroundMethod
New methods for estimating follow-up rates in cohort studies
Xue, X., Agalliu, I., Kim, M., Wang, T., Lin, J., Ghavamian, R., & Strickler, H. D. (2017) · BMC Medical Research Methodology · doi:10.1186/s12874-017-0436-z · Cited by 89
How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations
Junaid, K. P., Kiran, T., Gupta, M., Kishore, K., & Siwatch, S. (2025) · Population Health Metrics · doi:10.1186/s12963-025-00364-2 · Cited by 94
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Brilleman, S. L., Pachana, N. A., & Dobson, A. J. (2010) · BMC Medical Research Methodology · doi:10.1186/1471-2288-10-71 · Cited by 88
“Random effects models and generalised estimating equations may be used for longitudinal analysis with data missing from participants who have dropped out [12].”
Method
Evidence and practice in spine registries
van Hooff, M. L., Jacobs, W. C. H., Willems, P. C. P. H., Wouters, M. W. J. M., de Kleuver, M., Peul, W. C., Ostelo, R., & Fritzell, P. (2015) · Acta Orthopaedica · doi:10.3109/17453674.2015.1043174 · Cited by 88
Blood–spinal cord barrier permeability in experimental spinal cord injury: dynamic contrast‐enhanced MRI
Cohen, D. M., Patel, C. B., Ahobila‐Vajjula, P., Sundberg, L. M., Chacko, T., Liu, S., & Narayana, P. A. (2009) · NMR in Biomedicine · doi:10.1002/nbm.1343 · Cited by 96
“, GEE can handle cases with missing data without the need for imputation (30).”
A novel imputation methodology for time series based on pattern sequence forecasting
Bokde, N. D., Beck, M. W., Alvarez, F. M., & Kulat, K. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.09.020 · Cited by 81
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Dueñas, M., Ramirez, C., Arana, R., & Failde, I. (2011) · BMC Cardiovascular Disorders · doi:10.1186/1471-2261-11-24 · Cited by 83
“If a particular subject is missing one or more out of T repeated measurements, the remaining available data from the other measurements for that particular subject are used in the analyses [26].”
Background
Th2 Cytokines Are Associated with Persistent Hepatic Fibrosis in HumanSchistosoma japonicumInfection
Coutinho, H. M., Acosta, L. P., Wu, H. W., McGarvey, S. T., Su, L., Langdon, G., Jiz, M., Jarilla, B. R., Olveda, R. M., Friedman, J. F., et al. (2006) · The Journal of Infectious Diseases · doi:10.1086/510313 · Cited by 83
“puted using the last-value-carried-forward method [20].”
Impact of neoadjuvant chemoradiotherapy on health-related quality of life in long-term survivors of esophageal or junctional cancer: results from the randomized CROSS trial
Noordman, B. J., Verdam, M. G. E., Lagarde, S. M., Shapiro, J., Hulshof, M. C. C. M., Henegouwen, M. I. V. B., Wijnhoven, B. P. L., Nieuwenhuijzen, G. A. P., Bonenkamp, J. J., Cuesta, M. A., et al. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx726 · Cited by 83
Research Design in Clinical Psychology
Kazdin, A. E. (2021) · doi:10.1017/9781108993647 · Cited by 240
Effect of Physical Inactivity on Cognitive Performance after 2.5 Years of Follow-Up
Aichberger, M. C., Busch, M. A., Reischies, F. M., Ströhle, A., Heinz, A., & Rapp, M. A. (2010) · GeroPsych · doi:10.1024/1662-9647/a000003 · Cited by 83
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Oliver, M., Witten, K., KEARNS, R. A., Mavoa, S., Badland, H. M., Carroll, P., Drumheller, C., Tava’e, N., Asiasiga, L., Jelley, S., et al. (2011) · BMC Public Health · doi:10.1186/1471-2458-11-587 · Cited by 81
“Moreover, GEE models handle unbalanced and data missing at random [57].”
Background
Precipitation and Temperature Effects on Mortality and Lactation Parameters of Dairy Cattle in California
Stull, C. L., Messam, L. L. M., Collar, C. A., Peterson, N. G., Castillo, A. R., Reed, B., Andersen, K. L., & VerBoort, W. R. (2008) · Journal of Dairy Science · doi:10.3168/jds.2008-1215 · Cited by 81
“Missing test day values were imputed longitudinally by linear interpolation (Twisk and de Vente, 2002).”
Method
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Hall, M., Holton, K. M., Öngür, D., Montrose, D. M., & Keshavan, M. S. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2019.02.003 · Cited by 89
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Kempen, G. I. J. M., & VANSONDEREN, E. (2002) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(01)00474-7 · Cited by 81
Pragmatic, Cluster Randomized Trial of a Policy to Introduce Low‐Low Beds to Hospital Wards for the Prevention of Falls and Fall Injuries
Haines, T. P., Bell, R., & Varghese, P. N. (2010) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2010.02735.x · Cited by 79
“GEEs are similar to repeated-measures analysis of variance (ANOVA) but can be adapted for a range of data distributions for the dependent variable.(24) A negative binomial family with log link function and exchangeable working correlation structure for the GEE analyses was employed.”
Method
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Hovenkamp-Hermelink, J. H. M., van der Veen, D. C., Oude Voshaar, R. C., Batelaan, N. M., Penninx, B. W. J. H., Jeronimus, B. F., Schoevers, R. A., & Riese, H. (2019) · Scientific Reports · doi:10.1038/s41598-019-39931-7 · Cited by 73
CopyMean: A new method to predict monotone missing values in longitudinal studies
Genolini, C., Lacombe, A., Écochard, R., & Subtil, F. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.04.010 · Cited by 52
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Productive Engagement Among Older Americans: Prevalence, Patterns, and Implications for Public Policy
Hinterlong, J. E. (2008) · Journal of Aging & Social Policy · doi:10.1080/08959420801977491 · Cited by 72
HMG-CoA Reductase Inhibitors and the Risk of Vertebral Fracture
Schoofs, M. W. C. J., Sturkenboom, M., van der Klift, M., Albert Hofman, D., Pols, H. A. P., & Stricker, B. H. C. (2004) · Journal of Bone and Mineral Research · doi:10.1359/jbmr.040607 · Cited by 72
“The combined variance accounts for the uncertainty introduced by estimating the missing values.((34)) In the original data set, there were no missing values for age, gender, and length of follow-up, and 170 (4.”
Background
Simple imputation methods were inadequate for missing not at random (MNAR) quality of life data
Fielding, S., Fayers, P. M., McDonald, A. M., McPherson, G. C., & Campbell, M. (2008) · Health and Quality of Life Outcomes · doi:10.1186/1477-7525-6-57 · Cited by 71
Experienced burden of and adherence to smartphone-based ecological momentary assessment in persons with affective disorders
van Genugten, C. R., Schuurmans, J., Lamers, F., Riese, H., Penninx, B. W. J. H., Schoevers, R. A., Riper, H. M., & Smit, J. H. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9020322 · Cited by 71
“A more detailed description about GEE models is described in detail elsewhere [28,29].”
Method
Social Change and the Health of Sexual Minority Individuals: Do the Effects of Minority Stress and Community Connectedness Vary by Age Cohort?
Frost, D. M., Meyer, I. H., Lin, A., Wilson, B. D. M., Lightfoot, M., Russell, S. T., & Hammack, P. L. (2022) · Archives of Sexual Behavior · doi:10.1007/s10508-022-02288-6 · Cited by 71
“GEEs do not require dropping the entirety of a participant’s data if they did not participate in all waves (case-wise deletion), and GEEs do not require imputation of missing data for missing waves (Twisk & de Vente, 2002).”
Background
Dealing With Missing Data
Sainani, K. L. (2015) · PM&R · doi:10.1016/j.pmrj.2015.07.011 · Cited by 68
“Simulations show that multiple imputation along with maximum likelihood methods (described later) are optimal for most common missing data scenarios [1-4].”
Method
The differentiated effectiveness of a printed versus a Web-based tailored physical activity intervention among adults aged over 50
Peels, D. A., van Stralen, M. M., Bolman, C. A. W., Golsteijn, R. H. J., de Vries, H., Mudde, A. N., & Lechner, L. (2014) · Health Education Research · doi:10.1093/her/cyu039 · Cited by 72
“to an incomplete dataset have been shown to give more accurate estimations than applying imputation methods [51].”
Method

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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