Danielle Burke

34 papers receiving 2.6k citations

Danielle Burke's Hit Papers

Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes 2018 · 626 citations
6260+3+6Years since publication200400600

Peers

Danielle Burke
Comparison fields: 5 of 163
  • Statistics, Probability and Uncertainty 211
  • Statistics and Probability 166
  • Pulmonary and Respiratory Medicine 493
  • Urology 81
  • Pediatrics, Perinatology and Child Health 222
Replace Neil Hawkins with:
Neil Hawkins United Kingdom
Susan Dutton United Kingdom
Tom Palmer United Kingdom
Alexander Strasak Austria
Feng Xie Canada
Gerd Antes Germany
W. Dale Plummer United States
Évelyne Decullier France
Fares Alahdab United States
Paola Berchialla Italy
Danielle Burke relative to Neil Hawkins United Kingdom Neil Hawkins's profile →
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Countries citing papers authored by Danielle Burke

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

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.

Border = papers with Danielle Burke Line = papers co-authored together Danielle Burke links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes
Hit paper breakdown →
2018626
2 2006344
3
Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ
Hit paper breakdown →
2016285
4 2013218
5 2017180
6 2018176
7 2009137
8 202178
9 200473
10 201866
11 201665
12 200958
13 201336
14 201926
15 200923
16 202221
17 201819
18 202019
19 201818
20 201616

About Danielle Burke

Danielle Burke is a scholar working on Statistics, Probability and Uncertainty, Statistics and Probability, Pulmonary and Respiratory Medicine, Surgery and Oncology, having authored 35 papers that have together received 2.6k indexed citations. Recurring topics across this work include Meta-analysis and systematic reviews (11 papers), Statistical Methods in Clinical Trials (6 papers), Statistical Methods and Bayesian Inference (5 papers), Pulmonary Hypertension Research and Treatments (4 papers), Neonatal Respiratory Health Research (3 papers), Osteoarthritis Treatment and Mechanisms (2 papers), Malaria Research and Control (2 papers) and Cancer Cells and Metastasis (2 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (211 citations), Statistics and Probability (166 citations), Pulmonary and Respiratory Medicine (493 citations), Urology (81 citations) and Pediatrics, Perinatology and Child Health (222 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, Frank E. Harrell, Karel G.M. Moons, Gary S. Collins, Kurt R. Stenmark, Maria G. Frid, Todd C. Carpenter and Neil Davie. 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 BMC Medical Research Methodology.

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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