Mason McComb
Impact in
- Statistics and Probability top 10%
- Statistical Methods in Clinical Trials
Papers in
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- Multiple Sclerosis Research Studies 3
-
- Computational Drug Discovery Methods 3
- Co-authors
- Murali Krishna Ramanathan (6 shared papers)Robert R. Bies (1 shared paper)Jens Kühle (3 shared papers)Robert Zivadinov (3 shared papers)Niels Bergsland (2 shared papers)Eva Kubala Havrdova (1 shared paper)Michael G. Dwyer (1 shared paper)Manuela Vaněčková (1 shared paper)
- Journals
- Multiple Sclerosis and Related Disorders (2 papers)British Journal of Clinical Pharmacology (1 paper)The Analyst (1 paper)Journal of clinical lipidology (1 paper)Clinical Cancer Research (1 paper)
- Partner nations
- United StatesSwitzerlandItaly
In The Last Decade
Mason McComb
11 papers receiving 330 citations
Peers
Comparison fields: 5 of 73
- Statistics and Probability 44
- Health Informatics 5
- Pathology and Forensic Medicine 46
- Immunology 44
- Computational Theory and Mathematics 36
Countries citing papers authored by Mason McComb
This map shows the geographic impact of Mason McComb'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 Mason McComb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mason McComb more than expected).
Fields of papers citing papers by Mason McComb
This network shows the impact of papers produced by Mason McComb. 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 Mason McComb. The network helps show where Mason McComb may publish in the future.
Co-authors
The 25 scholars most cited alongside Mason McComb, 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 | 2021 | 98 | |
| 2 | 2020 | 66 | |
| 3 | 2024 | 57 | |
| 4 | 2013 | 27 | |
| 5 | 2020 | 20 | |
| 6 | 2023 | 17 | |
| 7 | 2019 | 16 | |
| 8 | 2020 | 15 | |
| 9 | 2021 | 12 | |
| 10 | 2021 | 7 | |
| 11 | Nonlinear Mixed Effects Models in Population PK/PD [R package nlmixr version 2.0.4] | 2021 | 1 |
About Mason McComb
Mason McComb is a scholar working on Pathology and Forensic Medicine, Computational Theory and Mathematics, Statistics and Probability, Developmental Neuroscience and Neurology, having authored 11 papers that have together received 336 indexed citations. Recurring topics across this work include Multiple Sclerosis Research Studies (3 papers), Computational Drug Discovery Methods (3 papers), Statistical Methods in Clinical Trials (2 papers), Metabolomics and Mass Spectrometry Studies (2 papers), Microfluidic and Bio-sensing Technologies (1 paper), Prostate Cancer Treatment and Research (1 paper), Kruppel-like factors research (1 paper) and Cholesterol and Lipid Metabolism (1 paper). The work is most often cited by research in Statistics and Probability (44 citations), Health Informatics (5 citations), Pathology and Forensic Medicine (46 citations), Immunology (44 citations) and Computational Theory and Mathematics (36 citations). Mason McComb has collaborated with scholars based in United States, Switzerland and Italy. Frequent co-authors include Murali Krishna Ramanathan, Robert R. Bies, Jens Kühle, Robert Zivadinov, Niels Bergsland, Eva Kubala Havrdova, Michael G. Dwyer, Manuela Vaněčková, Christian Barro and Tomáš Uher. Their work appears in journals such as Multiple Sclerosis and Related Disorders, British Journal of Clinical Pharmacology, The Analyst, Journal of clinical lipidology and Clinical Cancer Research.
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.