Missing Data Methods
Missing Data Methods is a statistical methodology within epidemiologic research that addresses the systematic absence of data values by providing principled approaches to estimate, impute, or account for missing observations under explicit assumptions about their mechanism.
7.1K
Publications
733.1K
Citations
17.2K
Authors
4.4K
Institutions
Publications per year
2017–2026
2.3K
7.1K
Missing data: Our view of the state of the art.
Joseph L. Schafer, J. A. Graham · Psychological Methods · 2002 · 10.9K citations
1–5 of 7.1K
17.2K
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London School of Hygiene & Tropical Medicine, United Kingdom
59 publications · 16.2K citations
44
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36
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4.4K
Harvard University
Cambridge, United States
73 h-index
429 publications
194K citations
London School of Hygiene & Tropical Medicine
London, United Kingdom
60 h-index
244 publications
38.6K citations
University College London
London, United Kingdom
54 h-index
218 publications
34.2K citations
University of California, Los Angeles
Los Angeles, United States
54 h-index
196 publications
31.8K citations
University of Michigan
Ann Arbor, United States
51 h-index
283 publications
23.9K citations
MRC Biostatistics Unit
Cambridge, United Kingdom
46 h-index
109 publications
33.3K citations

Johns Hopkins University
Baltimore, United States
45 h-index
231 publications
44.3K citations
MRC Clinical Trials Unit at UCL
London, United Kingdom
44 h-index
87 publications
31.6K citations
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284 publications · 66.7K citations
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354 publications · 35.2K citations
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206 publications · 27.8K citations
61
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128 publications · 10.2K citations
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87 publications · 10.7K citations
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