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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“Only missing data at Time 1 were imputed because LMMs can handle missing follow-up data points of a participant as long as values are missing (completely) at random (Twisk et al., 2013).”
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Oudenhoven, L. M., van Vulpen, L. F., Dallmeijer, A. J., de Groot, S., Buizer, A. I., & van der Krogt, M. M. (2019) · Gait & Posture · doi:10.1016/j.gaitpost.2019.06.023 · Cited by 14
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“Graham thus suggests that imputing missing data is a better approach compared to listwise deletion in most circumstances, regardless of the model of missingness (i.e., MCAR, MAR, MNAR; see Graham, 2009; but also see Twisk et al., 2013).”
Repeated onabotulinum neurotoxin A injections for drooling in children with neurodisability
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“Moreover, the use of a mixed-model enabled analysis with missing data, thereby reducing the possible bias associated with multiple imputations (Twisk et al. 2013).”
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“Moreover, it can be safely used without conducting multiple imputations beforehand [43].”
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“This was in line with previous studies showing that imputations are not needed before performing a mixed model analysis on longitudinal data.(17) In the mixed model, patients were not excluded from the analysis if a variable was missing at some, but not all, time points after baseline.”
ResultBarends, H., Dekker, J., van Dessel, N. C., Twisk, J. W. R., van der Horst, H. E., & van der Wouden, J. C. (2023) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2023.111343 · Cited by 10
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“MLM was ideal for the structure of our data for several reasons (Twisk et al., 2013).”
MethodManaging cancer and living meaningfully (CALM): Implementation in Dutch cancer care
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“For instance, imputation methods encompass mean or median imputation (Lodder et al., 2013), regression-based imputation (Crambes & Henchiri, 2019), hot-deck imputations (Andridge & Little, 2010), and multiple imputation techniques (Twisk et al., 2013).”
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“Furthermore, mixed modeling analysis is flexible in handling missing data, and applying multilevel analysis to an incomplete data set is even better than applying imputation methods (24).”
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“C. Imputation with Regression This is an imputation technique that uses information from the observed data to replace the missing values with predicted values from a regression model.”
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“Data from participants with incomplete VAS arm pain scores were included, multiple imputation was deemed unnecessary for mixed-model analysis of longitudinal data [25].”
MAGAN: A masked autoencoder generative adversarial network for processing missing IoT sequence data
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Major, M. E., van Egmond, M. A., Dettling-Ihnenfeldt, D. S., Ramaekers, S. P. J., Engelbert, R., & van der Schaaf, M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284097 · Cited by 9
“Independent t-tests or MannWhitney U tests (as appropriate) were applied comparing outcome data and covariates of complete and incomplete cases [34] followed by plotting of longitudinal changes in primary and secondary outcomes for complete cases versus data for the total study sample.”
MethodVerhoeven, M. M. A., Tekstra, J., Marijnissen, A. C. A., Meier, A. J. L., Westgeest, A. A. A., Lafeber, F. P. J. G., Jacobs, J. W. G., van Laar, J. M., & Welsing, P. M. J. (2021) · Rheumatology Advances in Practice · doi:10.1093/rap/rkab004 · Cited by 9
Staff, A. I., der Oord, S. V., Oosterlaan, J., Hornstra, R., Hoekstra, P. J., van den Hoofdakker, B. J., & Luman, M. (2022) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-021-00892-z · Cited by 8
“Missing data was random (≤ 5%) for all outcomes, and was taken into account in multilevel analysis (Twisk et al., 2013).”
MethodBijnsdorp, F. M., van der Beek, A. J., Groenou, M. I. B. V., Proper, K. I., van den Heuvel, S. G., & Boot, C. R. L. (2022) · Scandinavian Journal of Work Environment & Health · doi:10.5271/sjweh.4014 · Cited by 9
“Also, research showed that imputation of missing values is not necessary before performing a longitudinal mixed-model analyses, regardless of the missing data mechanism (30).”
BackgroundGonzález-Robles, A., Roca, P., Díaz‐García, A., García‐Palacios, A., & Botella, C. (2022) · JMIR Mental Health · doi:10.2196/40268 · Cited by 9
“necessary before performing longitudinal mixed model analysis [40].”
Kim, Y., Choi, Y., Kim, S., Park, Y.-S., Oh, I., Kim, K., & Kim, Y. (2015) · Complementary Therapies in Medicine · doi:10.1016/j.ctim.2015.04.004 · Cited by 9
Pacella, K. A. C., Forbush, K. T., Chen, Y., Nation, M. B., Cushing, C. C., & Swinburne-Romine, R. (2024) · International Journal of Eating Disorders · doi:10.1002/eat.24291 · Cited by 7
Iohexol clearance, but not estimated GFR, reveals a steeper GFR decline in patients with prediabetes
Melsom, T., Brobak, K. M., Norvik, J. V., Enoksen, I. T., Rinde, L. B., Jenssen, T. G., & Eriksen, B. O. (2025) · Kidney International · doi:10.1016/j.kint.2025.06.019 · Cited by 8
Wu, J., Yao, L., Liao, K. F., Yorke, J., Yu, Y., Liu, Z., Yao, X., & Qin, Z. (2026) · Chinese Medical Journal · doi:10.1097/cm9.0000000000004084 · Cited by 8
“[31] The change score was defined as the difference between two measurement time points for individuals at each PGI-C level.”
A Novel Imputation Approach for Sharing Protected Public Health Data
Erdman, E. A., Young, L. D., Bernson, D., Bauer, C., Chui, K. K. H., & Stopka, T. J. (2021) · American Journal of Public Health · doi:10.2105/ajph.2021.306432 · Cited by 8
Deguire, F., López‐Arango, G., Knoth, I. S., Côté, V., Agbogba, K., & Lippé, S. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac027 · Cited by 8
Ben, Â. J., van Dongen, J. M., Alili, M. E., Heymans, M. W., Twisk, J. W. R., Vroomen, J. L. M., de Wit, M., van Dijk, S. E. M., Oosterhuis, T., & Bosmans, J. E. (2022) · The European Journal of Health Economics · doi:10.1007/s10198-022-01525-y · Cited by 11
“Previous studies have shown that when opting for LLM, in case of reasonably normally distributed outcomes, MI is not necessary to obtain unbiased estimates, because the maximum likelihood function uses all observed data and produces unbiased estimates under the MAR assumption [14, 15].”
BackgroundMethodResultInfluentialSelf-citationFive-year illness trajectories across racial groups in the UK following a first episode psychosis
Griffiths, S. L., Bogatsu, T., Longhi, M., Butler, E., Alexander, B., Bandawar, M., Everard, L., Jones, P. B., Fowler, D. G., Hodgekins, J., et al. (2023) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-023-02428-w · Cited by 8
“In such situations where mechanisms of missingness are assumed to be random, the REML algorithm (used within the analysis) is shown to be robust to large missing data and unbalanced designs [25, 28, 29].”
MethodTransparency and reproducibility in the Adolescent Brain Cognitive Development (ABCD) study
Lopez, D. A., Cardenas‐Iniguez, C., Subramaniam, P., Adise, S., Bottenhorn, K. L., Badilla, P., Mukwekwerere, E. D., Tally, L., Ahanmisi, O., Bedichek, I. L., et al. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101408 · Cited by 10
Stam, H., van Vugt, V. A., Twisk, J. W. R., Finne‐Soveri, H., Garms-Homolová, V., Declercq, A., Jónsson, P. V., Onder, G., van der Roest, H. G., van Hout, H. P. J., et al. (2019) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2019.09.008 · Cited by 8
Self-citationMorris, A. S., Murphy, R., Hopkins, N. D., Low, D. A., Healy, G. N., Edwardson, C. L., Collins, B., Timpson, H., Shepherd, S. O., Cochrane, M., et al. (2020) · Journal of Occupational and Environmental Medicine · doi:10.1097/jom.0000000000002066 · Cited by 8
“In line with objective two to derive estimates of the preliminary effect of the interventions on worktime sitting, linear mixed modeling was conducted to compare intervention effects at 3 and 10 months from baseline.(50) Behavioral data were analyzed statistically using STATA (Timberlake Consultants Limited, UK) with…”
MethodPersistent Use of Prescription Opioids Before and After Lumbar Spine Surgery
Holmberg, S. T., Skurtveit, S. O., Gulati, S., Salvesen, Ø. O., Nygaard, O. P., Solberg, T. K., & Fredheim, O. M. S. (2023) · Spine · doi:10.1097/brs.0000000000004710 · Cited by 8
Tibi, L., van Oppen, P., van Balkom, A. J. L. M., Eikelenboom, M., Visser, H. A. D., & Anholt, G. E. (2023) · Australian & New Zealand Journal of Psychiatry · doi:10.1177/00048674231173342 · Cited by 8
“Missing data were handled using the full information maximum likelihood estimation (Twisk et al., 2013).”
MethodAlavi, D. H., Henriksen, H. B., Lauritzen, P. M., Zucknick, M., Bøhn, S. K., Henriksen, C., Paur, I., Smeland, S., & Blomhoff, R. (2023) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2023.06.037 · Cited by 8
Intention-to-Treat Analysis in Clinical Research
Armijo-Olivo, S. L., da Silva, J. B., de Castro‐Carletti, E. M., de Oliveira‐Souza, A. I. S., Pelai, E. B., Mohamad, N., Baghbaninaghadehi, F., Dennett, L., Steen, J. P., Kumbhare, D. A., et al. (2024) · American Journal of Physical Medicine & Rehabilitation · doi:10.1097/phm.0000000000002444 · Cited by 8
Bakker, M. H., Hugtenburg, J. G., Bet, P. M., Twisk, J. W. R., van der Horst, H. E., & Slottje, P. (2025) · British Journal of General Practice · doi:10.3399/bjgp.2024.0173 · Cited by 8
“As such, all models included all available values, took into account the dependency of repeated measurements within an individual, and were capable of handling missing data 49 in a longitudinal dataset without the need to perform multiple imputations.”
Self-citationClustering Algorithm for Incomplete Data Sets with Mixed Numeric and Categorical Attributes
Sen, W., Chen, H., & Xiaodong, F. (2013) · International Journal of Database Theory and Application · doi:10.14257/ijdta.2013.6.5.09 · Cited by 7
Brunet, A., Sapkota, R. P., Guragain, B., Tremblay, J., Saumier, D., & Kirmayer, L. J. (2021) · BMC Psychiatry · doi:10.1186/s12888-021-03441-6 · Cited by 10
“Since mixed-model analysis can handle missing data [43], imputation prior to analysis was not necessary for the PP sample.”
MethodLichtenstein, E., Held, S., Rappelt, L., Zacher, J., Eibl, A., Ludyga, S., Faude, O., & Donath, L. (2023) · European Review of Aging and Physical Activity · doi:10.1186/s11556-023-00331-6 · Cited by 7
“Data were analyzed using an intention-to-treat approach and missing data were handled by utilizing the mixed effects model that is robust against missing data [23].”
The Bone, Exercise, Alendronate, and Caloric Restriction (BEACON) trial design and methods
Beavers, K. M., Wolle, B. R., Ard, J. D., Beavers, D. P., Biehl, O., Brubaker, P. H., Burghardt, A. J., Calderone, C. T., Carballido‐Gamio, J., Fanning, J. T., et al. (2024) · Contemporary Clinical Trials · doi:10.1016/j.cct.2024.107692 · Cited by 7
Changes in social relationships from 26 to 34 years of age in adults born very preterm
Gonen, E., Twilhaar, E. S., Baumann, N., Busch, B., Bartmann, P., & Dieter Wolke, C. (2024) · Paediatric and Perinatal Epidemiology · doi:10.1111/ppe.13133 · Cited by 8
Ferrés, S., Serrat, M., Auer, W., Royuela-Colomer, E., Almirall, M., Lizama-Lefno, A., Nijs, J., Maes, M. H. J., Luciano, J. V., Borràs, X., et al. (2024) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2024.12.149 · Cited by 7
Using interleaved practice to foster spelling acquisition
Klimovich, M., & Richter, T. (2025) · Learning and Instruction · doi:10.1016/j.learninstruc.2025.102146 · Cited by 7
Pugliese, J. M., Coyle, P. C., Knox, P. J., Sions, J. M., Patterson, C. G., Pohlig, R. T., Simon, C. B., Weiner, D. K., George, S. Z., Piva, S. R., et al. (2021) · Physical Therapy · doi:10.1093/ptj/pzab255 · Cited by 8
Rausch‐Koster, P. T., Rennert, K. N., Heymans, M. W., Verbraak, F. D., van Rens, G. H. M. B., & van Nispen, R. M. A. (2022) · Ophthalmic and Physiological Optics · doi:10.1111/opo.12984 · Cited by 7
“57 Another limitation is the relatively small group of patients with diabetic macular oedema (8%), which may limit the results of this study in regard to this particular group.”
Self-citationHartman, L. M., Alili, M. E., Cutolo, M., Opriș-Belinski, D., Silva, J. D., Szekanecz, Z., Buttgereit, F., Masaryk, P., Bos, R., Kok, M. R., et al. (2022) · Seminars in Arthritis and Rheumatism · doi:10.1016/j.semarthrit.2022.152109 · Cited by 8
Moderators Influencing the Effectiveness of a Behavioral Teacher Program
Veenman, B., Luman, M., & Oosterlaan, J. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00298 · Cited by 7
“Missing data were not imputed as multilevel analyses adequately deal with missing data (Twisk et al., 2013).”
MethodAlbers, W. M. M., Roeg, D. P. K., Nijssen, Y. A. M., Bongers, I. M. B., & van Weeghel, J. (2018) · BMC Psychiatry · doi:10.1186/s12888-018-1831-7 · Cited by 8
Surgery for extraforaminal lumbar disc herniation: a single center comparative observational study
Polak, S. B., Madsbu, M. A., Vangen-Lønne, V., Salvesen, Ø. O., Nygaard, O. P., Solberg, T. K., Vleggeert-Lankamp, C. L. A., & Gulati, S. (2020) · Acta Neurochirurgica · doi:10.1007/s00701-020-04313-w · Cited by 7
Missing item responses in latent growth analysis: Item response theory versus classical test theory
Gorter, R., Fox, J.-P., Eekhout, I. E., Heymans, M. W., & Twisk, J. W. R. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280219897706 · Cited by 7
“A general procedure can be applied to obtain parameter estimates of the LCM model using multiple imputations for the latent variable under the IRT model (equation (6)) and CTT model (equation (12)), while accounting for missing item responses using multiple imputations for the missing item scores.”
BackgroundMethodInfluentialSelf-citationDanielsen, E., Mjåset, C., Ingebrigtsen, T., Gulati, S., Grotle, M., Rudolfsen, J. H., Nygaard, O. P., & Solberg, T. K. (2022) · Scientific Reports · doi:10.1038/s41598-022-17194-z · Cited by 7
A Longitudinal Examination of Work–Family Conflict among Working Mothers in the United States
Raza, H., van Eeden‐Moorefield, B., Grzywacz, J. G., Linver, M. R., & Lee, S. (2018) · Emerald Group Publishing Limited eBooks · doi:10.1108/s1530-353520180000013007 · Cited by 7
Boudreau, F., Dagenais, G. R., de Vries, H., Walthouwer, M. J. L., Côté, J., Turbide, G., Bourlaud, A.-S., & Poirier, P. (2020) · Health Psychology and Behavioral Medicine · doi:10.1080/21642850.2020.1850287 · Cited by 6
“Moreover, linear mixed models are better able to handle missing observations (Peters et al., 2012; Twisk, de Boer, de Vente, & Heymans, 2013).”
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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