Diane Uschner

972 citations
31 papers · 544 · h-index 12

Impact in

Papers in

    • Statistical Methods in Clinical Trials 11
    • Statistical Methods and Bayesian Inference 5
    • Statistical Methods and Inference 5
    • Advanced Causal Inference Techniques 4
    • SARS-CoV-2 and COVID-19 Research 5
    • COVID-19 Clinical Research Studies 2

Diane Uschner

28 papers receiving 537 citations

Peers

Diane Uschner
Comparison fields: 5 of 89
  • Statistics and Probability 129
  • Family Practice 9
  • Endocrinology, Diabetes and Metabolism 68
  • Geriatrics and Gerontology 8
  • Genetics 64
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Citations per year

Countries citing papers authored by Diane Uschner

Since Specialization
Citations

This map shows the geographic impact of Diane Uschner'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 Diane Uschner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diane Uschner more than expected).

Fields of papers citing papers by Diane Uschner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Diane Uschner. 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 Diane Uschner. The network helps show where Diane Uschner may publish in the future.

Co-authors

The 25 scholars most cited alongside Diane Uschner, 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 Diane Uschner Line = papers co-authored together Diane Uschner links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201893
2 202164
3 201756
4 202155
5 201851
6 201838
7 202123
8 202222
9 202219
10 201815
11 202414
12 202212
13 202211
14 201811
15 201911
16 202310
17 20239
18 20235
19 20234
20 20194

About Diane Uschner

Diane Uschner is a scholar working on Statistics and Probability, Infectious Diseases, Artificial Intelligence, Endocrinology, Diabetes and Metabolism and Management Science and Operations Research, having authored 31 papers that have together received 544 indexed citations. Recurring topics across this work include Statistical Methods in Clinical Trials (11 papers), Statistical Methods and Bayesian Inference (5 papers), Statistical Methods and Inference (5 papers), SARS-CoV-2 and COVID-19 Research (5 papers), Advanced Causal Inference Techniques (4 papers), Diabetes Management and Research (3 papers), Optimal Experimental Design Methods (2 papers) and COVID-19 Clinical Research Studies (2 papers). The work is most often cited by research in Statistics and Probability (129 citations), Family Practice (9 citations), Endocrinology, Diabetes and Metabolism (68 citations), Geriatrics and Gerontology (8 citations) and Genetics (64 citations). Diane Uschner has collaborated with scholars based in United States, Germany and Austria. Frequent co-authors include R.-D Hilgers, N Heussen, William F. Rosenberger, Paula M. Trief, Ruth S. Weinstock, Marcel Binnebösel, Toine M. Lodewick, Kerstine Carter, Jonathan Chipman and Ulf P. Neumann. Their work appears in journals such as Statistics in Biopharmaceutical Research, Statistics in Medicine, BMC Medical Research Methodology, Pediatric Nephrology and Journal of General Internal Medicine.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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