Brandon Arnieri
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Statistics and Probability top 5%
- Statistical Methods in Clinical Trials
- Advanced Causal Inference Techniques
Papers in
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- Statistical Methods in Clinical Trials 2
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- Cancer Genomics and Diagnostics 2
- Co-authors
- William B. Capra (5 shared papers)Samuel Whipple (3 shared papers)Gillis J. Carrigan (4 shared papers)Michael D. Taylor (4 shared papers)Ryan Copping (2 shared papers)Michael W. Lu (2 shared papers)Melisa Tucker (3 shared papers)Ying Lü (1 shared paper)
- Journals
- Pharmacoepidemiology and Drug Safety (1 paper)Health Services Research (1 paper)BMC Medical Research Methodology (1 paper)Nature (1 paper)European Journal of Cancer (1 paper)
- Partner nations
- United StatesSwitzerlandFrance
In The Last Decade
Brandon Arnieri
8 papers receiving 560 citations
Brandon Arnieri's Hit Papers
Peers
Comparison fields: 5 of 65
- Health Informatics 58
- Statistics and Probability 107
- Cancer Research 67
- Oncology 100
- Economics and Econometrics 83
Countries citing papers authored by Brandon Arnieri
This map shows the geographic impact of Brandon Arnieri'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 Brandon Arnieri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brandon Arnieri more than expected).
Fields of papers citing papers by Brandon Arnieri
This network shows the impact of papers produced by Brandon Arnieri. 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 Brandon Arnieri. The network helps show where Brandon Arnieri may publish in the future.
Co-authors
The 25 scholars most cited alongside Brandon Arnieri, 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 | Evaluating eligibility criteria of oncology trials using real-world data and AI Hit paper breakdown → | 2021 | 233 |
| 2 | 2018 | 179 | |
| 3 | 2019 | 82 | |
| 4 | 2019 | 46 | |
| 5 | 2019 | 26 | |
| 6 | 2014 | 5 | |
| 7 | 2016 | 4 | |
| 8 | 2018 | 1 |
About Brandon Arnieri
Brandon Arnieri is a scholar working on Statistics and Probability, Cancer Research, Pulmonary and Respiratory Medicine, Genetics and Radiology, Nuclear Medicine and Imaging, having authored 8 papers that have together received 576 indexed citations. Recurring topics across this work include Lung Cancer Treatments and Mutations (2 papers), Cancer Genomics and Diagnostics (2 papers), Statistical Methods in Clinical Trials (2 papers), Ethics in Clinical Research (1 paper), Cancer Treatment and Pharmacology (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper), PI3K/AKT/mTOR signaling in cancer (1 paper) and Lung Cancer Diagnosis and Treatment (1 paper). The work is most often cited by research in Health Informatics (58 citations), Statistics and Probability (107 citations), Cancer Research (67 citations), Oncology (100 citations) and Economics and Econometrics (83 citations). Brandon Arnieri has collaborated with scholars based in United States, Switzerland and France. Frequent co-authors include William B. Capra, Samuel Whipple, Gillis J. Carrigan, Michael D. Taylor, Ryan Copping, Michael W. Lu, Melisa Tucker, Ying Lü, James Zou and Navdeep Pal. Their work appears in journals such as Pharmacoepidemiology and Drug Safety, Health Services Research, BMC Medical Research Methodology, Nature and European Journal of Cancer.
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.