Chris Harbron
Impact in
- Health Informatics top 5%
- Statistics and Probability top 5%
- Statistical Methods in Clinical Trials
Papers in
- Oncology 9
- PARP inhibition in cancer therapy 4
-
- Computational Drug Discovery Methods 6
- Co-authors
- Elizabeth A. Harrington (2 shared papers)Mark Wappett (2 shared papers)Margaret H. Veldman-Jones (2 shared papers)Catherine Geh (1 shared paper)Roz Brant (1 shared paper)Claire Rooney (1 shared paper)Gayle Marshall (1 shared paper)Hollie Emery (1 shared paper)
- Journals
- Pharmaceutical Statistics (5 papers)Cancer Research (4 papers)Statistics in Medicine (2 papers)Blood (2 papers)Nature Machine Intelligence (2 papers)
- Partner nations
- United KingdomSwitzerlandGermany
In The Last Decade
Chris Harbron
39 papers receiving 929 citations
Peers
Comparison fields: 5 of 131
- Health Informatics 22
- Statistics and Probability 112
- Cancer Research 123
- Pathology and Forensic Medicine 113
- Oncology 162
Countries citing papers authored by Chris Harbron
This map shows the geographic impact of Chris Harbron'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 Chris Harbron with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris Harbron more than expected).
Fields of papers citing papers by Chris Harbron
This network shows the impact of papers produced by Chris Harbron. 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 Chris Harbron. The network helps show where Chris Harbron may publish in the future.
Co-authors
The 25 scholars most cited alongside Chris Harbron, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 240 | |
| 2 | 2011 | 69 | |
| 3 | 2014 | 54 | |
| 4 | 2021 | 48 | |
| 5 | 2021 | 44 | |
| 6 | 2017 | 44 | |
| 7 | 2023 | 36 | |
| 8 | 2012 | 35 | |
| 9 | 2007 | 34 | |
| 10 | 2011 | 31 | |
| 11 | 2016 | 29 | |
| 12 | 2010 | 26 | |
| 13 | 2020 | 26 | |
| 14 | 2023 | 25 | |
| 15 | 2014 | 23 | |
| 16 | 2022 | 22 | |
| 17 | 2019 | 20 | |
| 18 | 2013 | 20 | |
| 19 | 2022 | 15 | |
| 20 | 2016 | 15 |
About Chris Harbron
Chris Harbron is a scholar working on Oncology, Computational Theory and Mathematics, Statistics and Probability, Molecular Biology and Pathology and Forensic Medicine, having authored 42 papers that have together received 948 indexed citations. Recurring topics across this work include Statistical Methods in Clinical Trials (8 papers), Computational Drug Discovery Methods (6 papers), Lymphoma Diagnosis and Treatment (6 papers), Chronic Lymphocytic Leukemia Research (5 papers), Health Systems, Economic Evaluations, Quality of Life (4 papers), PARP inhibition in cancer therapy (4 papers), Pharmaceutical Economics and Policy (3 papers) and Monoclonal and Polyclonal Antibodies Research (3 papers). The work is most often cited by research in Health Informatics (22 citations), Statistics and Probability (112 citations), Cancer Research (123 citations), Pathology and Forensic Medicine (113 citations) and Oncology (162 citations). Chris Harbron has collaborated with scholars based in United Kingdom, Switzerland and Germany. Frequent co-authors include Elizabeth A. Harrington, Mark Wappett, Margaret H. Veldman-Jones, Catherine Geh, Roz Brant, Claire Rooney, Gayle Marshall, Hollie Emery, Alan Sharpe and J. Carl Barrett. Their work appears in journals such as Pharmaceutical Statistics, Cancer Research, Statistics in Medicine, Blood and Nature Machine Intelligence.
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.