Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls2009 · 5.6k citations
What are hit papers?
Paper score: A paper's citations measured against the top-1% bar of its own field and year: the square root of citations ÷ bar, so 1 means right at the bar and 2 means four times its citations. The bar blends the citation counts that enter the top 1% of the paper's subfields in its year (weighted 89.5%), of those subfields over all years (0.5%) and of its year across all fields (10%); a subfield and year with fewer than 400 papers takes its year's bar. Papers from 2026 are not scored yet: in an unfinished year, when a paper appeared matters more than how it is cited.
Hit paper: A paper cited at least 1.5× the top-1% bar of its field and year: a paper score of at least √1.5.
Countries citing papers authored by Michael Spratt
Since Specialization
Citations
This map shows the geographic impact of Michael Spratt's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael Spratt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Spratt more than expected).
This network shows the impact of papers produced by Michael Spratt. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael Spratt. The network helps show where Michael Spratt may publish in the future.
Co-authors
The 19 scholars most cited alongside Michael Spratt, linked wherever they
have co-authored with each other. Click a name or a connecting line to browse the papers
they share.
Border = papers with Michael SprattLine = papers co-authored togetherMichael Spratt links everyone, so they are left out of the graph.
All Works
5 of 5 papers shown
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Work
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Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
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research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
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