The DOI matches its Crossref record.
Multiple Imputation and its Application
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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“The sensitivity of the final results to missing data will be investigated using multiple imputation analysis based on chained equations and predictive mean matching.63”
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Factors associated with COVID-19-related death using OpenSAFELY
Williamson, E. J., Walker, A. J., Bhaskaran, K., Bacon, S., Bates, C., Morton, C. E., Curtis, H. J., Mehrkar, A., Evans, D., Inglesby, P., et al. (2020) · Nature · doi:10.1038/s41586-020-2521-4 · Cited by 6,674
“We conducted an additional sensitivity analysis using a population-calibrated imputation approach to handle missing ethnicity 49,50 , with marginal proportions of each ethnicity group within each of nine broad geographical regions of England (East, East Midlands, London, North East, North West,…”
MethodmissMDA : A Package for Handling Missing Values in Multivariate Data Analysis
Josse, J., & Husson, F. (2016) · Journal of Statistical Software · doi:10.18637/jss.v070.i01 · Cited by 1,423
Missing Data in Clinical Research: A Tutorial on Multiple Imputation
Austin, P. C., White, I. R., Lee, D. S., & van Buuren, S. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2020.11.010 · Cited by 1,006
Missing data and multiple imputation in clinical epidemiological research
Pedersen, A. B., Mikkelsen, E. M., Cronin-Fenton, D. P., Kristensen, N. R., Pham, T. M., Pedersen, L. H., & Petersen, I. (2017) · Clinical Epidemiology · doi:10.2147/clep.s129785 · Cited by 982
Accounting for missing data in statistical analyses: multiple imputation is not always the answer
Hughes, R. A., Heron, J., Sterne, J. A. C., & Tilling, K. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz032 · Cited by 802
Metadata
- Authors
- Carpenter, J. R., & Kenward, M. G.
- ORCID
- Carpenter, James R.
- Venue
- Wiley eBooks
- Publisher
- Wiley
- Published
- 2012-12-18
- 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
- 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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