Glen A. Satten

18.9k total citations · 1 hit paper
146 papers, 13.8k citations indexed

About

Glen A. Satten is a scholar working on Statistics and Probability, Genetics and Molecular Biology. According to data from OpenAlex, Glen A. Satten has authored 146 papers receiving a total of 13.8k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Statistics and Probability, 39 papers in Genetics and 31 papers in Molecular Biology. Recurrent topics in Glen A. Satten's work include Genetic Associations and Epidemiology (33 papers), Statistical Methods and Bayesian Inference (26 papers) and HIV/AIDS Research and Interventions (26 papers). Glen A. Satten is often cited by papers focused on Genetic Associations and Epidemiology (33 papers), Statistical Methods and Bayesian Inference (26 papers) and HIV/AIDS Research and Interventions (26 papers). Glen A. Satten collaborates with scholars based in United States, Uganda and United Kingdom. Glen A. Satten's co-authors include Jack Fuhrer, Scott D. Holmberg, Frank J. Palella, Anne C. Moorman, Diane Aschman, Mark O. Loveless, Somnath Datta, Michael P. Epstein, Andrew S. Allen and Michael P. Busch and has published in prestigious journals such as New England Journal of Medicine, Proceedings of the National Academy of Sciences and The Lancet.

In The Last Decade

Glen A. Satten

144 papers receiving 13.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Declining Morbidity and Mortality among Patients with Advanced Human Immunodeficiency Virus Infection

1998 7.6k citations Glen A. Satten et al. New England Journal of Medicine profile →

Peers

Countries citing papers authored by Glen A. Satten

Since Specialization

Citations

This map shows the geographic impact of Glen A. Satten'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 Glen A. Satten with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Glen A. Satten more than expected).

Fields of papers citing papers by Glen A. Satten

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Glen A. Satten. 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 Glen A. Satten. The network helps show where Glen A. Satten may publish in the future.

Co-authorship network of co-authors of Glen A. Satten

This figure shows the co-authorship network connecting the top 25 collaborators of Glen A. Satten. A scholar is included among the top collaborators of Glen A. Satten based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Glen A. Satten. Glen A. Satten is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Compositional analysis of microbiome data using the linear decomposition model (LDM) 2023 ·Bioinformatics ·Yi‐Juan Hu, Glen A. Satten	3
2	A rarefaction-without-resampling extension of PERMANOVA for testing presence–absence associations in the microbiome 2022 ·Bioinformatics ·Yi‐Juan Hu, Glen A. Satten	11
3	Integrative analysis of relative abundance data and presence–absence data of the microbiome using the LDM 2022 ·Bioinformatics ·Zhengyi Zhu, Glen A. Satten, Yi‐Juan Hu	10
4	LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control 2022 ·Proceedings of the National Academy of Sciences ·Yingtian Hu, Glen A. Satten, Yi‐Juan Hu	21
5	Testing microbiome associations with survival times at both the community and individual taxon levels 2022 ·PLoS Computational Biology ·Yingtian Hu, (unknown), Glen A. Satten, Yi‐Juan Hu	6
6	A rarefaction-based extension of the LDM for testing presence–absence associations in the microbiome 2021 ·Bioinformatics ·Yi‐Juan Hu, (unknown), Glen A. Satten	15
7	Testing hypotheses about the microbiome using the linear decomposition model (LDM) 2020 ·Bioinformatics ·Yi‐Juan Hu, Glen A. Satten	62
8	Characterization of Bacterial Communities in Selected Smokeless Tobacco Products Using 16S rDNA Analysis 2016 ·PLoS ONE ·Robert E. Tyx, Stephen B. Stanfill, Lisa M. Keong, Angel J. Rivera, Glen A. Satten, Clifford H. Watson	56
9	Testing Rare-Variant Association without Calling Genotypes Allows for Systematic Differences in Sequencing between Cases and Controls 2016 ·PLoS Genetics ·Yi‐Juan Hu, (unknown), H. Richard Johnston, Andrew S. Allen, Glen A. Satten	20
10	Effect of population stratification on the identification of significant single-nucleotide polymorphisms in genome-wide association studies 2009 ·BMC Proceedings ·Sara M. Sarasua, Julianne S. Collins, Dhelia Williamson, Glen A. Satten, Andrew S. Allen	7
11	Response to Lee et al. 2008 ·The American Journal of Human Genetics ·Michael P. Epstein, Andrew S. Allen, Glen A. Satten	7
12	Investigating Childhood Leukemia in Churchill County, Nevada 2006 ·Environmental Health Perspectives ·Carol Rubin, Adrianne Holmes, Martin Belson, Robert L. Jones, W. Dana Flanders, Stephanie Kieszak, John Osterloh, (unknown), Benjamin C. Blount, Dana Boyd Barr, Karen Steinberg, Glen A. Satten, Michael A. McGeehin, Randall L. Todd	65
13	Abstracts from the Fourteenth Annual Meeting of the International Genetic Epidemiology Society 2005 ·Genetic Epidemiology ·Rodney H. Adkins, Chad K. Klauser, John N. Fain, Everett F. Magann, John C. Morrison, Andrew R. Allen, Glen A. Satten, Kristina Allen‐Brady, Joanna M. Biernacka, Heather J. Cordell, (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown)	1
14	Rank-Sum Tests for Clustered Data 2005 ·Journal of the American Statistical Association ·Somnath Datta, Glen A. Satten	153
15	Genetic Association Analysis Using Data from Triads and Unrelated Subjects 2005 ·The American Journal of Human Genetics ·Michael P. Epstein, Colin Veal, Richard C. Trembath, Juliet N. Barker, Chun Li, Glen A. Satten	63
16	Informative Missingness in Genetic Association Studies: Case-Parent Designs 2003 ·The American Journal of Human Genetics ·Andrew S. Allen, Paul J. Rathouz, Glen A. Satten	52
17	Accounting for Unmeasured Population Substructure in Case-Control Studies of Genetic Association Using a Novel Latent-Class Model 2001 ·The American Journal of Human Genetics ·Glen A. Satten, W. Dana Flanders, Quanhe Yang	192
18	Estimating the Extent of Tracking in Interval‐Censored Chain‐Of‐Events Data 1999 ·Biometrics ·Glen A. Satten	25
19	Use of immunological markers and continuous-time Markov models to estimate progression of HIV infection in homosexual men 1996 ·AIDS ·(unknown), Glen A. Satten, (unknown), (unknown), Peter Th.A. Schellekens, Roel A. Coutinho, Frits van Griensven	41
20	Estimated Risk of Transmission of the Human Immunodeficiency Virus by Screened Blood in the United States 1995 ·New England Journal of Medicine ·Eve Lackritz, Glen A. Satten, John Aberle‐Grasse, Roger Y. Dodd, Vincent Raimondi, Robert S. Janssen, (unknown), Edward P. Notari, Lyle R. Petersen	264

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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