Danielle Burke
Impact in
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- Meta-analysis and systematic reviews
- Statistics and Probability top 2%
- Statistical Methods in Clinical Trials
Papers in
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- Meta-analysis and systematic reviews 11
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- Statistical Methods in Clinical Trials 7
- Statistical Methods and Bayesian Inference 5
- Co-authors
- Richard D RileyJoie EnsorKym I E SnellKarel G.M. MoonsFrank E. HarrellGary S. CollinsKurt R. StenmarkMaria G. Frid
- Journals
- Statistics in Medicine (6 papers)American Journal of Physiology-Lung Cellular and Molecular Physiology (2 papers)Malaria Journal (2 papers)Statistical Methods in Medical Research (2 papers)CHEST Journal (1 paper)
- Partner nations
- United KingdomUnited StatesNetherlands
In The Last Decade
Danielle Burke
34 papers receiving 2.5k citations
Hit Papers
Peers
Comparison fields: 5 of 168
- Statistics, Probability and Uncertainty 232
- Statistics and Probability 215
- Urology 150
- Health Informatics 24
- Pulmonary and Respiratory Medicine 572
Countries citing papers authored by Danielle Burke
This map shows the geographic impact of Danielle Burke'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 Danielle Burke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danielle Burke more than expected).
Fields of papers citing papers by Danielle Burke
This network shows the impact of papers produced by Danielle Burke. 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 Danielle Burke. The network helps show where Danielle Burke may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Danielle Burke, 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 | 0 | |
| 2 | 2022 | 4 | |
| 3 | 2022 | 20 | |
| 4 | 2021 | 1 | |
| 5 | 2020 | 5 | |
| 6 | 2020 | 18 | |
| 7 | 2019 | 5 | |
| 8 | Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes Hit paper breakdown → | 2018 | 581 |
| 9 | 2018 | 66 | |
| 10 | 2018 | 18 | |
| 11 | 2017 | 15 | |
| 12 | 2017 | 6 | |
| 13 | 2017 | 178 | |
| 14 | 2014 | 9 | |
| 15 | 2013 | 211 | |
| 16 | 2009 | 58 | |
| 17 | 2009 | 23 | |
| 18 | 2009 | 137 | |
| 19 | 2006 | 340 | |
| 20 | 2004 | 73 |
About Danielle Burke
Danielle Burke is a scholar working on Statistics, Probability and Uncertainty, Statistics and Probability, Urology, Obstetrics and Gynecology and Pediatrics, Perinatology and Child Health, having authored 35 papers that have together received 2.5k indexed citations. Recurring topics across this work include Meta-analysis and systematic reviews (11 papers), Statistical Methods in Clinical Trials (7 papers), Statistical Methods and Bayesian Inference (5 papers), Pulmonary Hypertension Research and Treatments (4 papers), Neonatal Respiratory Health Research (3 papers), Economic and Environmental Valuation (2 papers), Child Nutrition and Water Access (2 papers) and Congenital Diaphragmatic Hernia Studies (2 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (232 citations), Statistics and Probability (215 citations), Urology (150 citations), Health Informatics (24 citations) and Pulmonary and Respiratory Medicine (572 citations). Danielle Burke has collaborated with scholars based in United Kingdom, United States and Netherlands. Frequent co-authors include Richard D Riley, Joie Ensor, Kym I E Snell, Karel G.M. Moons, Frank E. Harrell, Gary S. Collins, Kurt R. Stenmark, Maria G. Frid, Neil Davie and Todd C. Carpenter. Their work appears in journals such as Statistics in Medicine, American Journal of Physiology-Lung Cellular and Molecular Physiology, Malaria Journal, Statistical Methods in Medical Research and CHEST Journal.
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.