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Flexible Imputation of Missing Data
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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“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
In the citing paper
“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.”
In the citing paper
“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]
In the citing paper
“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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Friedrich, S., Antes, G., Behr, S., Binder, H., Brannath, W., Dumpert, F., Ickstadt, K., Kestler, H. A., Lederer, J., Leitgöb, H., et al. (2021) · Advances in Data Analysis and Classification · doi:10.1007/s11634-021-00455-6 · Cited by 99
“Furthermore, various procedures for imputing missing data are known in statistics, which can be used to complete the data depending on the existing context and expertise [117,123,143].”
BackgroundMethodMethadone is superior to fentanyl in treating neuropathic pain in patients with head-and-neck cancer
Haumann, J., Geurts, J. W., van Kuijk, S. M. J., Kremer, B., Joosten, E. A. J., & Everdingen, M. H. J. V. D. B.-V. (2016) · European Journal of Cancer · doi:10.1016/j.ejca.2016.06.025 · Cited by 77
Assessing the fit of structural equation models with multiply imputed data.
Enders, C. K., & Mansolf, M. A. (2016) · Psychological Methods · doi:10.1037/met0000102 · Cited by 74
“Rubin’s (1987a) pooling rules apply only to parameters and assume that, over repeated samples, estimates are normally distributed around some population value (van Buuren, 2012; White, Royston, & Wood, 2011).”
BackgroundMethodInfluentialNetten, A. P., Rieffe, C. J., Theunissen, S. C. P. M., Soede, W., Dirks, E., Korver, A. M. H., Konings, S., Oudesluys‐Murphy, A. M., Dekker, F. W., & Frijns, J. H. M. (2015) · International Journal of Pediatric Otorhinolaryngology · doi:10.1016/j.ijporl.2015.10.008 · Cited by 83
Roumie, C. L., Chipman, J., Min, J. Y., Hackstadt, A. J., Hung, A. M., Greevy, R. A., Grijalva, C. G., Elasy, T. A., & Griffin, M. R. (2019) · JAMA · doi:10.1001/jama.2019.13206 · Cited by 78
Boehme, H. M., Cann, D., & Isom, D. A. (2020) · Crime & Delinquency · doi:10.1177/0011128720974309 · Cited by 87
“Multiple imputation was employed to handle missing values (Rubin, 1987; van Buuren, 2012).”
MethodHoogland, J., IntHout, J., Belias, M., Rovers, M. M., Riley, R. D., Harrell, F. E., Moons, K. G. M., Debray, T. P. A., & Reitsma, J. B. (2021) · Statistics in Medicine · doi:10.1002/sim.9154 · Cited by 79
Nathwani, C. L., Wilkinson, J. J., Fry, G., Armstrong, R., Smith, D. J., & Ihlenfeld, C. (2022) · Mineralium Deposita · doi:10.1007/s00126-021-01086-9 · Cited by 83
“More sophisticated imputation methods exist such as those which use multi-variable regression, nearest neighbour approaches and non-parametric methods suitable for compositional data (e.g. Martín-Fernández 2003; van Buuren 2012).”
Method
Metadata
- Authors
- van Buuren, S.
- Venue
- Chapman & Hall/CRC eBooks
- Publisher
- Chapman and Hall/CRC
- Published
- 2012-03-29
- 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 › Mathematics › Statistics and Probability › Data Analysis with R
- Keywords
- missing data, multiple imputation, uncertainty quantification, missing data imputation
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