DR Mould
Impact in
- Statistics and Probability top 2%
- Statistical Methods in Clinical Trials
- Pharmacology top 2%
- Antibiotics Pharmacokinetics and Efficacy
- Pharmacogenetics and Drug Metabolism
Papers in
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- Statistical Methods in Clinical Trials 6
- Statistical Methods and Bayesian Inference 1
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- Pharmacogenetics and Drug Metabolism 4
- Co-authors
- Richard N. Upton (4 shared papers)Geert D’Haens (1 shared paper)Thierry Lavé (1 shared paper)Boy Frame (1 shared paper)Andrew M. Stein (1 shared paper)Karthik Venkatakrishnan (1 shared paper)Nitin Mehrotra (1 shared paper)René Bruno (1 shared paper)
- Journals
- CPT Pharmacometrics & Systems Pharmacology (4 papers)Clinical Pharmacology & Therapeutics (3 papers)
- Partner nations
- United StatesAustraliaSwitzerland
In The Last Decade
DR Mould
7 papers receiving 1.5k citations
DR Mould's Hit Papers
Peers
Comparison fields: 5 of 122
- Statistics and Probability 215
- Pharmacology 150
- Pharmacology 220
- Transplantation 32
- Modeling and Simulation 51
Countries citing papers authored by DR Mould
This map shows the geographic impact of DR Mould'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 DR Mould with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites DR Mould more than expected).
Fields of papers citing papers by DR Mould
This network shows the impact of papers produced by DR Mould. 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 DR Mould. The network helps show where DR Mould may publish in the future.
Co-authors
The 12 scholars most cited alongside DR Mould, 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 | Basic Concepts in Population Modeling, Simulation, and Model‐Based Drug Development—Part 2: Introduction to Pharmacokinetic Modeling Methods Hit paper breakdown → | 2013 | 684 |
| 2 | Basic Concepts in Population Modeling, Simulation, and Model‐Based Drug Development Hit paper breakdown → | 2012 | 364 |
| 3 | 2014 | 200 | |
| 4 | 2014 | 80 | |
| 5 | 2015 | 70 | |
| 6 | 2016 | 66 | |
| 7 | 2015 | 26 |
About DR Mould
DR Mould is a scholar working on Statistics and Probability, Pharmacology, Radiology, Nuclear Medicine and Imaging, Immunology and Genetics, having authored 7 papers that have together received 1.5k indexed citations. Recurring topics across this work include Statistical Methods in Clinical Trials (6 papers), Pharmacogenetics and Drug Metabolism (4 papers), Monoclonal and Polyclonal Antibodies Research (2 papers), Biosimilars and Bioanalytical Methods (2 papers), Chronic Lymphocytic Leukemia Research (1 paper), Analytical Chemistry and Chromatography (1 paper), Computational Drug Discovery Methods (1 paper) and Statistical Methods and Bayesian Inference (1 paper). The work is most often cited by research in Statistics and Probability (215 citations), Pharmacology (150 citations), Pharmacology (220 citations), Transplantation (32 citations) and Modeling and Simulation (51 citations). DR Mould has collaborated with scholars based in United States, Australia and Switzerland. Frequent co-authors include Richard N. Upton, Geert D’Haens, Thierry Lavé, Boy Frame, Andrew M. Stein, Karthik Venkatakrishnan, Nitin Mehrotra, René Bruno, Iñaki F. Trocóniz and Danièle Ouellet. Their work appears in journals such as CPT Pharmacometrics & Systems Pharmacology and Clinical Pharmacology & Therapeutics.
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.