The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis
Abstract
Background and objectives As a result of the development of sophisticated techniques, such as multiple imputation, the interest in handling missing data in longitudinal studies has increased enormously in past years. Within the field of longitudinal data analysis, there is a current debate on whether it is necessary to use multiple imputations before performing a mixed-model analysis to analyze the longitudinal data. In the current study this necessity is evaluated.Study design and setting The results of mixed-model analyses with and without multiple imputation were compared with each other. Four data sets with missing values were created-one data set with missing completely at random, two data sets with missing at random, and one data set with missing not at random). In all data sets, the relationship between a continuous outcome variable and two different covariates were analyzed: a time-independent dichotomous covariate and a time-dependent continuous covariate.Results Although for all types of missing data, the results of the mixed-model analysis with or without multiple imputations were slightly different, they were not in favor of one of the two approaches. In addition, repeating the multiple imputations 100 times showed that the results of the mixed-model analysis with multiple imputation were quite unstable.Conclusion It is not necessary to handle missing data using multiple imputations before performing a mixed-model analysis on longitudinal data.
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In the citing paper
“Multiple imputation when performing mixed models analyses can lead to unstable results [26]”
In the citing paper
“Linear mixed modelling has been demonstrated to control for missing data, and therefore, multiple imputation was not required (Peters et al., 2012;Twisk et al., 2013).”
From Exercise combined with Acceptance and Commitment Therapy for chronic pain: One-year follow-up from a randomized controlled trial (2024) · cites this paper as Twisk et al., 2013
In the citing paper
“Twisk et al 71 provided evidence that multiple imputation for missing data is not necessary before computing longitudinal mixed models.”
In the citing paper
“However, completely missing measurements will not be imputed as this is not necessary for multilevel analysis (63).”
From Timing of imagery rescripting during schema therapy for borderline personality disorder: the LUCY trial (2023) · cites this paper as [63]
In the citing paper
“Therefore, patterns of missingness were not found to be MNAR and the decision to continue the analysis with the available data was made [84,85].”
From Negative and positive affect regulation in a transdiagnostic internet-based protocol for emotional disorders: Randomized controlled trial (2021) · cites this paper as [84]
In the citing paper
“Mixed model analysis handles missing data; no imputation method was applied since there are reports that imputations before a mixed model analysis is not necessary and simulations indicated that imputations before mixed models could result in unstable results [30].”
From Is sleep disturbance in patients with chronic pain affected by physical exercise or ACT-based stress management? - A randomized controlled study (2018) · cites this paper as [30]
In the citing paper
“We also performed all analyses on the data for 100 imputations. Because there is a current debate whether it is necessary to perform multiple imputations in combination with mixed-model analyses in longitudinal studies [65], we also performed the LMM analyses without multiple imputations.”
From Long-Term Results of a Web-Based Guided Self-Help Intervention for Employees With Depressive Symptoms: Randomized Controlled Trial (2014) · cites this paper as [65]
In the citing paper
“Because of the current debate on the necessity of multiple imputations in combination with mixed-model analysis in longitudinal studies [65], we also performed mixed-model analysis without multiple imputations. The results were comparable, indicating that data were robust and multiple imputations may not have been needed.”
From Long-Term Results of a Web-Based Guided Self-Help Intervention for Employees With Depressive Symptoms: Randomized Controlled Trial (2014) · cites this paper as [65]
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“It has been proven that this method is even better than multiple imputation techniques (65).”
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“We did not conduct multiple imputation, as the linear mixed models described in our Statistical Analysis are capable of handling these types of variability.(26,27)”
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Gulati, S., Vangen-Lønne, V., Nygaard, O. P., Gulati, A. M., Hammer, T. A., Johansen, T. O., Peul, W. C., Salvesen, Ø. O., & Solberg, T. K. (2021) · Neurosurgery · doi:10.1093/neuros/nyab259 · Cited by 48
Wiklund, T., Linton, S. J., Alföldi, P., & Gerdle, B. (2018) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-018-2020-z · Cited by 45
“Mixed model analysis handles missing data; no imputation method was applied since there are reports that imputations before a mixed model analysis is not necessary and simulations indicated that imputations before mixed models could result in unstable results [30].”
MethodSakakibara, B. M., Lear, S. A., Barr, S. I., Goldsmith, C. H., Schneeberg, A., Silverberg, N. D., Yao, J. K., & Eng, J. J. (2021) · International Journal of Stroke · doi:10.1177/17474930211017699 · Cited by 48
van Buul, L. W., van der Steen, J. T., Achterberg, W. P., Schellevis, F., Essink, R. T. G. M., de Greeff, S. C., Natsch, S., Sloane, P. D., Zimmerman, S. I., Twisk, J. W. R., et al. (2015) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dkv051 · Cited by 45
Self-citationAre People With Whiplash-Associated Neck Pain Different From People With Nonspecific Neck Pain?
Anstey, R., Kongsted, A., Kamper, S. J., & Hancock, M. J. (2016) · Journal of Orthopaedic and Sports Physical Therapy · doi:10.2519/jospt.2016.6588 · Cited by 45
van Dijk, D., Dekker, M. J. H. J., Conemans, E. B., Wiepjes, C. M., de Goeij, E. G. M., Overbeek, K. A., Fisher, A. D., den Heijer, M., & T'Sjoen, G. G. (2019) · The Journal of Sexual Medicine · doi:10.1016/j.jsxm.2019.05.009 · Cited by 46
Launay, O., Rosenberg, A. R., Rey, D., Pouget, N., Michel, M., Reynes, J., Neau, D., Raffi, F., Piroth, L., Carrat, F., et al. (2016) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.0741 · Cited by 44
“Nadir CD4 count/μL, median (range) 197 (0-800) 272 (3-908) 206 (3-754) 311 (39-779) 180 (0-601) 218 (13-534)”
van der Loos, M. A. T. C., Hellinga, I., Vlot, M. C., Klink, D. T., den Heijer, M., & Wiepjes, C. M. (2020) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.4262 · Cited by 44
“However, by analyzing the data with a mixed model, the missing data were randomly imputed.((19)) Therefore, the effect of missing data was minimalized while maintaining the largest study sample.”
MethodSalmon, J., Arundell, L., Cerin, E., Ridgers, N. D., Hesketh, K. D., Daly, R. M., Dunstan, D. W., Brown, H. L., Gatta, J. D., Gatta, P. A. D., et al. (2022) · British Journal of Sports Medicine · doi:10.1136/bjsports-2022-105825 · Cited by 43
Matcham, F., Simblett, S. K., Leightley, D., Dalby, M., Siddi, S., Haro, J. M., Lamers, F., Penninx, B. W. J. H., Bruce, S., Nica, R. I., et al. (2022) · Psychological Medicine · doi:10.1017/s0033291722003671 · Cited by 45
Some data quality issues at ClinicalTrials.gov
Chaturvedi, N., Mehrotra, B., Kumari, S., Gupta, S., Subramanya, H. S., & Saberwal, G. (2019) · Trials · doi:10.1186/s13063-019-3408-2 · Cited by 45
Hill, J. J., & Keating, J. L. (2014) · Physical Therapy · doi:10.2522/ptj.20140273 · Cited by 43
Aarden, J. J., Reijnierse, E. M., van der Schaaf, M., van der Esch, M., Reichardt, L. A., van Seben, R., Bosch, J. A., Twisk, J. W. R., Maier, A. B., Engelbert, R. H. H., et al. (2021) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2020.12.006 · Cited by 43
Self-citationZoellner, J., Thomson, J. L., Landry, A. S., Anderson‐Lewis, C., Connell, C. L., Molaison, E. F., & Yadrick, K. (2014) · Preventing Chronic Disease · doi:10.5888/pcd11.130269 · Cited by 42
“Maximum likelihood estimation was used to handle missing data in the repeated measures (20).”
Jacobi, C., Beintner, I., Fittig, E., Trockel, M., Braks, K., Schade‐Brittinger, C., & Dempfle, A. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7668 · Cited by 42
“Multiple imputation before conducting longitudinal mixed effects modeling does not add value and is not necessary [46,47].”
Blom, J. W., van den Hout, W. B., den Elzen, W. P. J., Drewes, Y. M., Bleijenberg, N., Fabbricotti, I. N., Jansen, A. P. D., Kempen, G. I. J. M., Koopmans, R. T. C. M., Looman, W., et al. (2018) · Age and Ageing · doi:10.1093/ageing/afy091 · Cited by 42
Zhu, R., Fogelholm, M., Poppitt, S. D., Silvestre, M. P., Møller, G., Huttunen‐Lenz, M., Stratton, G., Sundvall, J., Råman, L., Jalo, E., et al. (2021) · Nutrients · doi:10.3390/nu13113916 · Cited by 43
“Missing data were not handled with multiple imputation because this method did not increase precision [28,29].”
MethodAlili, M. E., van Dongen, J. M., Eßer, J., Heymans, M. W., van Tulder, M. W., & Bosmans, J. E. (2022) · Health Economics · doi:10.1002/hec.4603 · Cited by 41
“First, studies have shown that multiple imputation is not required when analyzing partly missing, clinical effect data using a longitudinal mixed model analysis (Peters et al., 2012; Twisk et al., 2013).”
BackgroundSelf-citationSolberg, R. B., Steene-Johannessen, J., Anderssen, S. A., Ekelund, U., Säfvenbom, R., Haugen, T., Berntsen, S., Åvitsland, A., Lerum, Ø., Resaland, G. K., et al. (2021) · BMC Public Health · doi:10.1186/s12889-021-10901-x · Cited by 37
“Multiple imputation when performing mixed models analyses can lead to unstable results [26], and when the analysis was rerun on the imputed data the estimates were attenuated and some no longer significant.”
MethodSanabria-Mazo, J. P., Colomer-Carbonell, A., Borràs, X., Castaño-Asins, J. R., McCracken, L. M., Montero-Marin, J., Pérez-Aranda, A., Edo, S., Sanz, A., Feliu-Soler, A., et al. (2023) · The Journal of Pain · doi:10.1016/j.jpain.2023.04.008 · Cited by 40
Lonsdale, C., Lester, A., Owen, K., White, R. L., Moyes, I., Peralta, L. R., Kirwan, M., Maeder, A., Bennie, A., MacMillan, F., et al. (2015) · BMC Public Health · doi:10.1186/s12889-015-2583-7 · Cited by 46
Tic-Related Versus Tic-Free Obsessive-Compulsive Disorder
de Vries, F. E., Cath, D. C., Hoogendoorn, A. W., van Oppen, P., Glas, G. G., Veltman, D. J., van den Heuvel, O. A., & van Balkom, A. J. L. M. (2016) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.14m09736 · Cited by 41
Blood pressure and age-related GFR decline in the general population
Eriksen, B. O., Stefansson, V. T. N., Jenssen, T. G., Mathisen, U. D., Schei, J., Solbu, M. D., Wilsgaard, T., & Melsom, T. (2017) · BMC Nephrology · doi:10.1186/s12882-017-0496-7 · Cited by 41
“All of the subjects included in the baseline RENIS cohort were included in the linear mixed regression analyses regardless of whether they were examined at follow-up because linear mixed models allow for missing observations at one or more points in time [41, 42].”
MethodDwinger, S., Rezvani, F., Kriston, L., Herbarth, L., Härter, M. C., & Dirmaier, J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0236861 · Cited by 41
“If there were data missing due to lost to follow-up, there was no imputation done since mixed model analyses provide unbiased estimates under the assumption that data are missing at random conditional on the variables in the model [66].”
MethodTibi, L., van Oppen, P., van Balkom, A. J. L. M., Eikelenboom, M., Hendriks, G., & Anholt, G. E. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.12.028 · Cited by 42
Willemsen, L. A., Boogers, L. S., Wiepjes, C. M., Klink, D. T., van Trotsenburg, A. S. P., den Heijer, M., & Hannema, S. E. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgac571 · Cited by 39
Díaz-García, A., González-Robles, A., García-Palacios, A., Fernández-Álvarez, J., Castilla, D., Bretón, J. M., Baños, R. M., Quero, S., & Botella, C. (2021) · Journal of Medical Internet Research · doi:10.2196/21335 · Cited by 36
“Therefore, patterns of missingness were not found to be MNAR and the decision to continue the analysis with the available data was made [84,85].”
BackgroundRojas, B., Catalan, E., Diez, G., & Roca, P. (2023) · PLOS ONE · doi:10.1371/journal.pone.0287388 · Cited by 38
“Given that imputation of missing values is not necessary before performing longitudinal mixed-model analysis [48], we did not impute the missing data.”
MethodTjaden, C. D., Mulder, C. L., den Hollander, W., Castelein, S., Delespaul, P., Keet, R., van Weeghel, J., & Kroon, H. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2021.2880 · Cited by 37
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Morillo‐Sarto, H., Barceló‐Soler, A., Herrera‐Mercadal, P., Pantilie, B., Navarro‐Gil, M., García‐Campayo, J., & Montero‐Marín, J. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-031327 · Cited by 40
Campos, D., Bretón-López, J., Botella, C., Mira, A., Castilla, D., Mor, S., Baños, R., & Quero, S. (2019) · BMC Psychiatry · doi:10.1186/s12888-019-2060-4 · Cited by 32
Iversen, V. M., Vasseljen, O., Mork, P. J., Gismervik, S., Bertheussen, G. F., Salvesen, Ø. O., & Fimland, M. S. (2018) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.13091 · Cited by 37
Piva, S. R., Schneider, M., Moore, C. G., Catelani, M. B., Gil, A. B., Klatt, B. A., DiGioia, A. M., Almeida, G. J., Khoja, S. S., Sowa, G., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.0018 · Cited by 35
Yu, C. H. Y., Choi, D., Bruno, B. A., Thorpe, K. E., Straus, S. E., Cantarutti, P., Chu, K., Frydrych, P., Hoang‐Kim, A., Ivers, N. M., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/16984 · Cited by 36
“To account for missing data, we conducted a fully adjusted mixed effect model using repeated measurements [40-42].”
MethodBoekhout, J. M., Volders, E., Bolman, C. A. W., de Groot, R. H. M., & Lechner, L. (2021) · Journal of Aging and Health · doi:10.1177/08982643211015027 · Cited by 37
“Analyses were conducted on an intention-to-treat basis without any ad hoc imputation (Twisk et al., 2013).”
MethodBoevé, L. M. S., Hulshof, M. C. C. M., Verhagen, P. C. M. S., Twisk, J. W. R., Witjes, W. P. J., de Vries, P. W., van Moorselaar, R. J. A., van Andel, G., & Vis, A. N. (2020) · European Urology · doi:10.1016/j.eururo.2020.08.023 · Cited by 34
Self-citation
Metadata
- Authors
- Twisk, J., de Boer, M., de Vente, W., & Heymans, M.
- Affiliations
- Vrije Universiteit Amsterdam (NL)
- Corresponding author
- Twisk, Jos
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2013-09
- Volume
- 66
- Issue
- 9
- Pages
- 1022-1028
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W1974992958
- Cited by
- 568
- References
- 43
- Citations by year
- 2026: 58 · 2025: 68 · 2024: 74 · 2023: 57 · 2022: 61 · 2021: 52 · 2020: 46 · 2019: 45 · 2018: 27 · 2017: 26 · 2016: 21 · 2015: 19 · 2014: 10 · 2013: 4
- FWCI
- 15.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- Pure Amsterdam UMC (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- VU Research Portal (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- multiple imputation, missing data, longitudinal analysis, mixed models, missing completely at random, missing at random, missing not at random, time-varying covariates
- MeSH terms
- Data Interpretation, Statistical, Humans, Longitudinal Studies, Patient Dropouts, Research Design, Models, Statistical
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