Missing Data Review
Missing Data Review is a systematic methodological process within data analysis that identifies, assesses, and addresses the patterns and implications of absent or incomplete data entries in a dataset to ensure the validity and reliability of empirical findings.
27
Publications
1.9K
Citations
81
Authors
62
Institutions
Publications per year
2017–2026
11
Parents
27
Missing outcomes in randomized trials: addressing the dilemma.
Douglas G. Altman · PubMed · 2009 · 143 citations
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62

McMaster University
Hamilton, Canada
3 h-index
13 publications
1.3K citations
University of Kansas
Lawrence, United States
2 h-index
4 publications
650 citations
University of California, Los Angeles
Los Angeles, United States
2 h-index
2 publications
146 citations

American University of Beirut
Beirut, Lebanon
2 h-index
3 publications
218 citations

Washington University in St. Louis
St Louis, United States
2 h-index
3 publications
334 citations
Medizinische Hochschule Hannover
Hanover, Germany
2 h-index
2 publications
33 citations
Hospital for Sick Children
Toronto, Canada
2 h-index
2 publications
187 citations
California State University, Chico
Chico, United States
2 h-index
5 publications
158 citations
1–8 of 62
2 publications · 83 citations
2
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Repository · 2 publications · 155 citations
2
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1 publications · 28 citations
1
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PIJPSM · 1 publications · 13 citations
1
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IJBD · 1 publications · 65 citations
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1–5