Diane Uschner
Impact in
- Statistics and Probability top 2%
- Statistical Methods in Clinical Trials
- Advanced Causal Inference Techniques
- Statistical Methods and Bayesian Inference
- Statistical Methods and Inference
Papers in
-
- Statistical Methods in Clinical Trials 11
- Statistical Methods and Bayesian Inference 5
- Statistical Methods and Inference 5
- Advanced Causal Inference Techniques 5
- Co-authors
- N HeussenR.-D HilgersWilliam F. RosenbergerPaula M. TriefRuth S. WeinstockRoman EickhoffAndreas KrohOleksandr Sverdlov
- Journals
- Statistics in Biopharmaceutical Research (4 papers)Statistics in Medicine (3 papers)BMC Medical Research Methodology (2 papers)JAMA Network Open (1 paper)Statistical Papers (1 paper)
- Partner nations
- United StatesGermanyAustria
In The Last Decade
Diane Uschner
28 papers receiving 509 citations
Peers
Comparison fields: 5 of 98
- Statistics and Probability 148
- Family Practice 13
- Endocrinology, Diabetes and Metabolism 90
- Modeling and Simulation 20
- Geriatrics and Gerontology 14
Countries citing papers authored by Diane Uschner
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
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-authorship network
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.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2024 | 12 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 0 | |
| 6 | 2023 | 2 | |
| 7 | 2023 | 4 | |
| 8 | 2023 | 10 | |
| 9 | 2023 | 4 | |
| 10 | 2023 | 2 | |
| 11 | 2022 | 11 | |
| 12 | 2022 | 17 | |
| 13 | 2022 | 11 | |
| 14 | 2022 | 20 | |
| 15 | 2021 | 51 | |
| 16 | 2021 | 60 | |
| 17 | 2021 | 1 | |
| 18 | 2021 | 22 | |
| 19 | 2018 | 15 | |
| 20 | 2017 | 55 |
About Diane Uschner
Diane Uschner is a scholar working on Statistics and Probability, Family Practice, Modeling and Simulation, Endocrinology, Diabetes and Metabolism and Biological Psychiatry, having authored 31 papers that have together received 516 indexed citations. Recurring topics across this work include Statistical Methods in Clinical Trials (11 papers), Diabetes Management and Research (6 papers), Statistical Methods and Bayesian Inference (5 papers), SARS-CoV-2 and COVID-19 Research (5 papers), Statistical Methods and Inference (5 papers), Advanced Causal Inference Techniques (5 papers), COVID-19 Clinical Research Studies (4 papers) and Diabetes Management and Education (3 papers). The work is most often cited by research in Statistics and Probability (148 citations), Family Practice (13 citations), Endocrinology, Diabetes and Metabolism (90 citations), Modeling and Simulation (20 citations) and Geriatrics and Gerontology (14 citations). Diane Uschner has collaborated with scholars based in United States, Germany and Austria. Frequent co-authors include N Heussen, R.-D Hilgers, William F. Rosenberger, Paula M. Trief, Ruth S. Weinstock, Roman Eickhoff, Andreas Kroh, Oleksandr Sverdlov, Florian Ulmer and Jonathan Chipman. Their work appears in journals such as Statistics in Biopharmaceutical Research, Statistics in Medicine, BMC Medical Research Methodology, JAMA Network Open and Statistical Papers.
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.