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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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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Multiple Imputation and its Application

Abstract

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Integrity signals

Verification

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