Cited by 913Statistical Methods and Bayesian InferenceAdvanced Causal Inference TechniquesStatistical Methods in Clinical Trials

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Multiple Imputation and its Application

Carpenter, James R., Kenward, Michael G.·2013·Wiley eBooks

DOI 10.1002/9781119942283

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Abstract

A practical guide to analysing partially observed data. Collecting, analysing and drawing inferences from data is central to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the intended data. The literature on inference from the resulting incomplete data is now huge, and continues to grow both as methods are developed for large and complex data structures, and as increasing computer power and suitable software enable researchers to apply these methods. This book focuses on a particular statistical method for analysing and drawing inferences from incomplete data, called Multiple Imputation (MI). MI is attractive because it is both practical and widely applicable. The authors aim is to clarify the issues raised by missing data, describing the rationale for MI, the relationship between the various imputation models and associated algorithms and its application to increasingly complex data structures. Multiple Imputation and its Application: Discusses the issues raised by the analysis of partially observed data, and the assumptions on which analyses rest. Presents a practical guide to the issues to consider when analysing incomplete data from both observational studies and randomized trials. Provides a detailed discussion of the practical use of MI with real-world examples drawn from medical and social statistics. Explores handling non-linear relationships and interactions with multiple imputation, survival analysis, multilevel multiple imputation, sensitivity analysis via multiple imputation, using non-response weights with multiple imputation and doubly robust multiple imputation. Multiple Imputation and its Application is aimed at quantitative researchers and students in the medical and social sciences with the aim of clarifying the issues raised by the analysis of incomplete data data, outlining the rationale for MI and describing how to consider and address the issues that arise in its application.

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BackgroundMethod
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BackgroundSelf-citation
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Self-citation
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BackgroundMethod
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Background
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Background
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“10 / (b) The conditional posterior distribution of σ 2 is an inverse gamma distribution with a shape parameter of and a rate parameter of In the presence of missing data, initial values are critically important to Gibbs sampling (Carpenter and Kenward, 2013).”
Background
Overview of Federated Facility to Harmonize, Analyze and Management of Missing Data in Cohorts
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“This imputation model included all baseline demographics and outcome measures, and a total of 20 multiple imputations were generated (Carpenter & Kenward, 2013; Graham et al., 2007).”
Method
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“Since the MNAR distribution cannot be determined from the observed data, Carpenter and Kenward (2013) suggest starting from the MAR scenario and then adjusting the model using MNAR assumptions in order to examine whether the MAR assumption is sensitive to such changes.”
Method
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Dealing with deficient and missing data
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“If this assumption is violated the data are missing not at random (MNAR) and the results of MI estimations will be biased to some extent (Carpenter and Kenward, 2013).”
Background
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Response to Comments by Seaman et al. on “Analysis of Longitudinal Trials With Protocol Deviation: A Framework for Relevant, Accessible Assumptions, and Inference via Multiple Imputation,” Journal of Biopharmaceutical Statistics 23:1352–1371
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“However, if we wish to specify a distribution for β explicitly, then this too can be accommodated within this approach, as discussed, for example, in Carpenter and Kenward (2013), §10.4.1.”
MethodInfluentialSelf-citation
An Incremental Algorithm for Repairing Training Sets with Missing Values
van Stein, B., & Kowalczyk, W. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-40581-0_15 · Cited by 16
The Pattern of Social Fluidity within the British Class Structure: A Topological Model
Bukodi, E., Goldthorpe, J. H., & Kuha, J. (2016) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12234 · Cited by 16
“The idea of multiple imputation is due to Rubin (1987), and overviews of more recent developments have been given by Schafer (1997), van Buuren (2012) and Carpenter and Kenward (2013).”
Method
Socioeconomic inequality in recovery from poor physical and mental health in mid-life and early old age: prospective Whitehall II cohort study
Tanaka, A., Shipley, M. J., Welch, C. A., Groce, N. E., Marmot, M. G., Mika Kivimaki, B., Singh‐Manoux, A., & Brunner, E. J. (2018) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2017-209584 · Cited by 16
Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data
Vidotto, D., Vermunt, J. K., & Deun, K. V. (2018) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998618769871 · Cited by 14
Does domiciliary welfare rights advice improve health-related quality of life in independent-living, socio-economically disadvantaged people aged ≥60 years? Randomised controlled trial, economic and process evaluations in the North East of England
Howel, D., Moffatt, S., Haighton, C., Bryant, A., Becker, F., Steer, M., Lawson, S., Aspray, T., Milne, E. M. G., Vale, L. D., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0209560 · Cited by 19
“[36] For the primary outcome (CASP-19), multiple imputation, using iterative chained equations, was used to obtain a complete data set for the primary outcome at 12 and 24 months, conditional on survival to 12 or 24 months.[37] The variables considered for the multiple imputation model were those thought a priori to b…”
Method
Missing values imputation based on fuzzy C-Means algorithm for classification of chronic obstructive pulmonary disease (COPD)
Aristiawati, K., Siswantining, T., Sarwinda, D., et al. (2019) · AIP conference proceedings · doi:10.1063/1.5139149 · Cited by 16
Botulinum toxin type A versus anticholinergics for cervical dystonia
Rodrigues, F. B., Duarte, G. S., Castelão, M., Marques, R., Ferreira, J. J., Sampaio, C., Moore, A. P., & Costa, J. N. (2021) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd004312.pub3 · Cited by 16
Diagnostic with incomplete nominal/discrete data
Jelinek, H. F., Yatsko, A., Stranieri, A., et al. (2015) · Artificial Intelligence Research · doi:10.5430/air.v4n1p22 · Cited by 16
Health TAPESTRY Ontario: protocol for a randomized controlled trial to test reproducibility and implementation
Mangin, D. A., Lamarche, L., Oliver, D., Bomze, S., Borhan, S., Browne, T., Carr, T., Datta, J., Dolovich, L. R., Howard, M., et al. (2020) · Trials · doi:10.1186/s13063-020-04600-y · Cited by 16
Discontinuation of biologic DMARDs in a real-world population of patients with rheumatoid arthritis in remission: outcome and risk factors
Arnold, S., Jaeger, V. K., Scherer, A., et al. (2021) · Lara D. Veeken · doi:10.1093/rheumatology/keab343 · Cited by 15
A comparison of single imputation and multiple imputation methods for missing data in different oncogene expression profiles
Ye, W., Zhang, L., Zhang, W., et al. (2022) · Biostatistics & Epidemiology · doi:10.1080/24709360.2021.2023805 · Cited by 15
Risk factors for overweight and obesity after childhood acute lymphoblastic leukemia in North America and Switzerland: A comparison of two cohort studies
Belle, F. N., Schindera, C., Ansari, M., Armstrong, G. T., Popovic, M. B., Howell, R. M., Leisenring, W. M., Meacham, L. R., Rössler, J. K., Spycher, B. D., et al. (2023) · Cancer Medicine · doi:10.1002/cam4.6588 · Cited by 15
Visual analysis of missing data — To see what isn't there
Fernstad, S. J., & Glen, R. C. (2014) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2014.7042514 · Cited by 15
Assessing heterogeneous effects and their determinants via estimation of potential outcomes
Nilsson, A., Bonander, C., Strömberg, U., & Björk, J. (2019) · European Journal of Epidemiology · doi:10.1007/s10654-019-00551-0 · Cited by 15
A multiple imputation score test for model modification in structural equation models.
Mansolf, M. A., Jorgensen, T. D., & Enders, C. K. (2019) · Psychological Methods · doi:10.1037/met0000243 · Cited by 15
Does second-hand smoke associate with tooth loss among older Japanese? JAGES cross-sectional study
Umemori, S., Aida, J., Tsuboya, T., Tabuchi, T., Tonami, K., Nitta, H., Araki, K., & Kondo, K. (2020) · International Dental Journal · doi:10.1111/idj.12577 · Cited by 15
Missing Data Imputation With Baseline Information in Longitudinal Clinical Trials
Zhang, Y., Zimmer, Z., Xu, L., et al. (2020) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2020.1845234 · Cited by 15
Real-time imputation of missing predictor values in clinical practice
Nijman, S. W. J., Hoogland, J., Groenhof, T. K. J., Brandjes, M., Jacobs, J. J. L., Bots, M. L., Asselbergs, F. W., Moons, K. G. M., & Debray, T. P. A. (2020) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaa016 · Cited by 17
Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics
Si, Y., Heeringa, S., Johnson, D., Little, R. J. A., Liu, W., Pfeffer, F. T., & Raghunathan, T. E. (2021) · Journal of Survey Statistics and Methodology · doi:10.1093/jssam/smab038 · Cited by 18

Authors: Carpenter, J. R., & Kenward, M. G.
ORCID: Carpenter, James R.
Venue: Wiley eBooks
Publisher: Wiley
Published: 2012-12-18
DOI: 10.1002/9781119942283
OpenAlex: W1500944114
Cited by: 913
References: 0
Citations by year: 2026: 13 · 2025: 37 · 2024: 53 · 2023: 65 · 2022: 81 · 2021: 101 · 2020: 94 · 2019: 90 · 2018: 93 · 2017: 85 · 2016: 85 · 2015: 54 · 2014: 44 · 2013: 14 · 2012: 3
FWCI: 9.44
Type: book
Language: en
Open access: closed
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
Locations: Wiley eBooks (publishedVersion)
CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
LSHTM Research Online (London School of Hygiene and Tropical Medicine) (submittedVersion)
Wiley eBooks
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords: multiple imputation, missing data, incomplete data analysis, imputation models, observational studies, nonlinear relationship, survival analysis, sensitivity analysis, medical statistics, social statistics

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