Statistics in Biopharmaceutical Research

5.1k citations
711 papers · · active since 2016

Impact in

Papers in

Statistics in Biopharmaceutical Research

622 papers receiving 4.8k citations

Peers

Statistics in Biopharmaceutical Research
Comparison fields: 5 of 184
  • Statistics and Probability 3.2k
  • Management Science and Operations Research 932
  • Statistics, Probability and Uncertainty 461
  • Economics and Econometrics 1.2k
  • Health Informatics 24
Replace Journal of Pharmacokinetics and Pharmacodynamics with:
Journal of Pharmacokinetics and Pharmacodynamics United States
The International Journal of Biostatistics United States
Lifetime Data Analysis United States
Statistical Analysis and Data Mining The ASA Data Science Journal United States
Advances in Data Analysis and Classification Italy
Sequential Analysis United States
Perspectives in Clinical Research India
Annals of Data Science India
Mathematical Medicine and Biology A Journal of the IMA United Kingdom
Personalized Medicine United States
Statistics in Biopharmaceutical Research relative to Journal of Pharmacokinetics and Pharmacodynamics United States Journal of Pharmacokinetics and Pharmacodynamics's profile →
Citations per field
00.5×1.7×
Journal of Pharmacokinetics and Pharmacodynamics · 1×
Citations per year

Countries where authors publish in Statistics in Biopharmaceutical Research

Since Specialization
Citations

This map shows the geographic impact of research published in Statistics in Biopharmaceutical 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 papers published in Statistics in Biopharmaceutical Research with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Statistics in Biopharmaceutical Research more than expected).

Fields of papers published in Statistics in Biopharmaceutical Research

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Statistics in Biopharmaceutical Research. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Statistics in Biopharmaceutical Research.

About Statistics in Biopharmaceutical Research

The 711 papers published in Statistics in Biopharmaceutical Research in the last decades have received a total of 5.1k indexed citations . Papers published in Statistics in Biopharmaceutical Research usually cover Statistics and Probability (625 papers), Management Science and Operations Research (205 papers), Statistics, Probability and Uncertainty (93 papers), Economics and Econometrics (214 papers) and Computational Theory and Mathematics (65 papers) specifically the topics of Statistical Methods in Clinical Trials (559 papers), Health Systems, Economic Evaluations, Quality of Life (207 papers), Optimal Experimental Design Methods (199 papers), Advanced Causal Inference Techniques (193 papers), Statistical Methods and Inference (121 papers), Statistical Methods and Bayesian Inference (118 papers), Computational Drug Discovery Methods (57 papers) and Meta-analysis and systematic reviews (56 papers). The most active scholars publishing in Statistics in Biopharmaceutical Research are Scott R. Evans, Frank Bretz, Dean A. Follmann, Cong Chen, Willi Maurer, Robert A. Beckman, Thomas Permutt, Alex A. Dmitrienko, John J. Peterson and Qin Liu.

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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