Handling missing data in clinical research

Heymans, Martijn W.; Twisk, Jos W.R.

doi:10.1016/j.jclinepi.2022.08.016

Cited by 399Statistical Methods and Bayesian InferenceStatistical Methods in Clinical TrialsStatistical Methods in Epidemiology

Handling missing data in clinical research

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Heymans, Martijn W., Twisk, Jos W.R.·2022·Journal of Clinical Epidemiology

DOI 10.1016/j.jclinepi.2022.08.016

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Abstract

Because missing data are present in almost every study, it is important to handle missing data properly. First of all, the missing data mechanism should be considered. Missing data can be either completely at random (MCAR), at random (MAR), or not at random (MNAR). When missing data are MCAR, a complete case analysis can be valid. Also when missing data are MAR, in some situations a complete case analysis leads to valid results. However, in most situations, missing data imputation should be used. Regarding imputation methods, it is highly advised to use multiple imputations because multiple imputations lead to valid estimates including the uncertainty about the imputed values. When missing data are MNAR, also multiple imputations do not lead to valid results. A complication hereby is that it not possible to distinguish whether missing data are MAR or MNAR. Finally, it should be realized that preventing to have missing data is always better than the treatment of missing data.

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Protocol authors have a choice between various methods for handling anticipated missing data, including imputing data, fitting a mixed effects model to repeated measures data, or omitting participants.
“When planning to handle anticipated missing data, protocol authors have a choice between various methods, including imputing data, fitting a mixed effects model to repeated measures data, or omitting participants.428 429”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [429]
The mechanism resulting in missing data affects the risk of bias and decisions on how best to handle the missing data.
“The mechanism resulting in missing data affects the risk of bias and decisions on how best to handle the missing data.428 429”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [429]

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Authors: Heymans, M. W., & Twisk, J. W. R.
Affiliations: Amsterdam University Medical Centers (NL)
Corresponding author: Twisk, Jos W.R.
ORCID: Heymans, Martijn W.
Twisk, Jos W.R.
Venue: Journal of Clinical Epidemiology
Publisher: Elsevier BV
Published: 2022-11
Volume: 151
Pages: 185-188
ISSN: 0895-4356 (ISSN-L)
DOI: 10.1016/j.jclinepi.2022.08.016
OpenAlex: W4296631245
Cited by: 399
References: 27
Citations by year: 2026: 153 · 2025: 129 · 2024: 90 · 2023: 27
FWCI: 45.37
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Journal of Clinical Epidemiology (publishedVersion, cc-by)
PubMed (publishedVersion)
Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
Pure Amsterdam UMC (submittedVersion, cc-by)
Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
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: missing data, missing data mechanisms, complete case analysis, multiple imputation, missing at random, missing not at random, clinical research, imputation uncertainty
MeSH terms: Humans, Uncertainty

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