Flexible Imputation of Missing Data

van Buuren, Stef

doi:10.1201/b11826

Cited by 2,022Statistical Methods and Bayesian InferenceMarkov Chains and Monte Carlo MethodsData Analysis with R

Flexible Imputation of Missing Data

van Buuren, Stef·2012·Chapman & Hall/CRC eBooks

DOI 10.1201/b11826

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Abstract

Missing data form a problem in every scientific discipline, yet the techniques required to handle them are complicated and often lacking. One of the great ideas in statistical science-multiple imputation-fills gaps in the data with plausible values, the uncertainty of which is coded in the data itself. It also solves other problems, many of which a

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Missing item responses in completed questionnaires were filled in with multiple imputation using chained equations.
“Item-level missing data in the completed surveys were imputed using the method of multiple imputation (MI) by chained equations (van Buuren, 2012).”
From WHO World Mental Health Surveys International College Student Project: Prevalence and distribution of mental disorders. (2018) · cites this paper as van Buuren, 2012
Missing data were imputed using predictive mean matching and expectation maximization-two well-established imputation methods-and sensitivity analyses based on both imputation techniques led to very similar results.
“However, we imputed missing data using predictive mean matching and expectation maximization—two well-established imputation methods [42]-and sensitivity analyses based on both imputation techniques led to very similar results.”
From Cost-effectiveness and cost-utility of an Acceptance and Commitment Therapy intervention vs. a Cognitive Behavioral Therapy intervention for older adults with anxiety symptoms: A randomized controlled trial (2022) · cites this paper as [42]
All acquired data will be examined for inconsistencies and missing values, which will be replaced using multiple imputation methods if appropriate; a drop-out analysis will be performed; internal consistencies of newly translated inventories will be calculated; analyses will be intention-to-treat or completer-analyses; data-analysts will be blinded for group allocation.
“All acquired data will be examined with respect to potential inconsistencies and missing values, which will be replaced using multiple imputation methods [42], if appropriate. To detect potential systematic biases, a drop-out analysis will be performed.”
From HELP@APP: Development and evaluation of a self-help app for traumatized Syrian refugees in Germany- A study protocol of a randomized controlled trial (2019) · cites this paper as [42]
Multiple imputation by chained equations was used to adjust for within-survey item non-response, random internal subsampling of survey sections, and missing data due to skip logic errors.
“In addition, multiple imputation (MI) by chained equations(Van Buuren, 2012) was used to adjust for within-survey item non-response, random internal subsampling of survey sections, and missing data due to skip logic errors that occurred in a few surveys.”
From Severe role impairment associated with mental disorders: Results of the WHO World Mental Health Surveys International College Student Project (2018) · cites this paper as Van Buuren, 2012

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Authors: van Buuren, S.
Venue: Chapman & Hall/CRC eBooks
Publisher: Chapman and Hall/CRC
Published: 2012-03-29
DOI: 10.1201/b11826
OpenAlex: W612972800
Cited by: 2,022
References: 0
Citations by year: 2026: 81 · 2025: 69 · 2024: 69 · 2023: 119 · 2022: 111 · 2021: 268 · 2020: 296 · 2019: 222 · 2018: 204 · 2017: 164 · 2016: 140 · 2015: 144 · 2014: 81 · 2013: 44 · 2012: 9
FWCI: 22.42
Type: book
Language: en
Open access: closed
Locations: Chapman & Hall/CRC eBooks (publishedVersion)
CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
Physical Sciences › Computer Science › Artificial Intelligence › Data Analysis with R
Keywords: missing data, multiple imputation, uncertainty quantification, missing data imputation

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Flexible Imputation of Missing Data

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