Anna McGlothlin

4.8k total citations
16 papers, 504 citations indexed

About

Anna McGlothlin is a scholar working on Statistics and Probability, Genetics and Oncology. According to data from OpenAlex, Anna McGlothlin has authored 16 papers receiving a total of 504 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Statistics and Probability, 5 papers in Genetics and 5 papers in Oncology. Recurrent topics in Anna McGlothlin's work include Statistical Methods in Clinical Trials (6 papers), Chronic Lymphocytic Leukemia Research (4 papers) and Cancer Genomics and Diagnostics (4 papers). Anna McGlothlin is often cited by papers focused on Statistical Methods in Clinical Trials (6 papers), Chronic Lymphocytic Leukemia Research (4 papers) and Cancer Genomics and Diagnostics (4 papers). Anna McGlothlin collaborates with scholars based in United States, Canada and Cyprus. Anna McGlothlin's co-authors include Kert Viele, Kristine Broglio, Margaret Foster, Donald A. Berry, Melanie Quintana, Susan Dent, Daniel Rayson, Scott Berry, Karen A. Gelmon and Christine Brezden‐Masley and has published in prestigious journals such as JAMA, Journal of Clinical Oncology and Molecular Cancer Therapeutics.

In The Last Decade

Anna McGlothlin

15 papers receiving 499 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Anna McGlothlin United States 9 189 179 92 64 56 16 504
Pei‐Fang Su Taiwan 11 126 0.7× 55 0.3× 40 0.4× 17 0.3× 87 1.6× 60 435
Sandrine Micallef France 10 68 0.4× 67 0.4× 292 3.2× 29 0.5× 16 0.3× 24 678
Ohad Amit United States 9 125 0.7× 57 0.3× 87 0.9× 14 0.2× 59 1.1× 11 556
Zhimin Yang China 11 129 0.7× 52 0.3× 22 0.2× 22 0.3× 18 0.3× 25 397
John Dwyer United Kingdom 10 53 0.3× 73 0.4× 97 1.1× 12 0.2× 31 0.6× 21 418
Panpan Wang China 9 46 0.2× 71 0.4× 42 0.5× 17 0.3× 48 0.9× 19 663
Christine Veyrat‐Follet France 15 174 0.9× 65 0.4× 129 1.4× 18 0.3× 58 1.0× 31 673
Dewi Rahardja United States 8 62 0.3× 75 0.4× 64 0.7× 31 0.5× 15 0.3× 44 258
O Abe Japan 9 336 1.8× 270 1.5× 14 0.2× 80 1.3× 47 0.8× 45 736
Matt Hutmacher United States 11 72 0.4× 28 0.2× 155 1.7× 18 0.3× 35 0.6× 15 618

Countries citing papers authored by Anna McGlothlin

Since Specialization
Citations

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

Fields of papers citing papers by Anna McGlothlin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anna McGlothlin. 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 Anna McGlothlin. The network helps show where Anna McGlothlin may publish in the future.

Co-authorship network of co-authors of Anna McGlothlin

This figure shows the co-authorship network connecting the top 25 collaborators of Anna McGlothlin. A scholar is included among the top collaborators of Anna McGlothlin 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 Anna McGlothlin. Anna McGlothlin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Picozzi, Vincent J., Anna M. Varghese, Paul E. Oberstein, et al.. (2025). Pamrevlumab plus nab-paclitaxel/gemcitabine (Pam + GA) as first- and second-line therapy in metastatic pancreatic cancer (mPDAC): Results from Precision Promise (PrP) Bayesian platform trial.. Journal of Clinical Oncology. 43(4_suppl). 673–673. 1 indexed citations
2.
Oberstein, Paul E., Andrea Wang‐Gillam, Anna M. Varghese, et al.. (2024). Precision Promise (PrP) Bayesian platform trial for metastatic pancreatic cancer (mPDAC): Results of the first experimental arm, SM-88 as second line therapy.. Journal of Clinical Oncology. 42(3_suppl). 675–675.
3.
Broglio, Kristine, Lale Kostakoglu, Federico Mattiello, et al.. (2022). PET-CR as a potential surrogate endpoint in untreated DLBCL: meta-analysis and implications for clinical trial design. Leukemia & lymphoma. 63(12). 2816–2831. 3 indexed citations
4.
Li, Xiaoyun, Kristine Broglio, Paul Bycott, et al.. (2021). Practical Considerations and Recommendations for Master Protocol Framework: Basket, Umbrella and Platform Trials. Therapeutic Innovation & Regulatory Science. 55(6). 1145–1154. 33 indexed citations
5.
VanBuren, John M., T. Charles Casper, Daniel K. Nishijima, et al.. (2021). The design of a Bayesian adaptive clinical trial of tranexamic acid in severely injured children. Trials. 22(1). 769–769. 6 indexed citations
6.
Viele, Kert, Benjamin R. Saville, Anna McGlothlin, & Kristine Broglio. (2020). Comparison of response adaptive randomization features in multiarm clinical trials with control. Pharmaceutical Statistics. 19(5). 602–612. 20 indexed citations
7.
Meier, Richard, et al.. (2019). Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-the-Losers Design. The American Statistician. 75(1). 66–75. 1 indexed citations
8.
Viele, Kert, Kristine Broglio, Anna McGlothlin, & Benjamin R. Saville. (2019). Comparison of methods for control allocation in multiple arm studies using response adaptive randomization. Clinical Trials. 17(1). 52–60. 31 indexed citations
9.
Hager, David N., Michael H. Hooper, Gordon R. Bernard, et al.. (2019). The Vitamin C, Thiamine and Steroids in Sepsis (VICTAS) Protocol: a prospective, multi-center, double-blind, adaptive sample size, randomized, placebo-controlled, clinical trial. Trials. 20(1). 197–197. 40 indexed citations
10.
McGlothlin, Anna & Kert Viele. (2018). Bayesian Hierarchical Models. JAMA. 320(22). 2365–2365. 69 indexed citations
11.
Infante, Jeffrey R., Amita Patnaik, Claire F. Verschraegen, et al.. (2017). Two Phase 1 dose-escalation studies exploring multiple regimens of litronesib (LY2523355), an Eg5 inhibitor, in patients with advanced cancer. Cancer Chemotherapy and Pharmacology. 79(2). 315–326. 29 indexed citations
12.
Broglio, Kristine, Melanie Quintana, Margaret Foster, et al.. (2016). Association of Pathologic Complete Response to Neoadjuvant Therapy in HER2-Positive Breast Cancer With Long-Term Outcomes. JAMA Oncology. 2(6). 751–751. 229 indexed citations
13.
Viele, Kert, Anna McGlothlin, & Kristine Broglio. (2016). Interpretation of Clinical Trials That Stopped Early. JAMA. 315(15). 1646–1646. 21 indexed citations
14.
Verschraegen, Claire F., Roger B. Cohen, Anthony J. Olszanski, et al.. (2011). Abstract A86: A phase 1 study of LY2523355, an Eg5 inhibitor, administered on Days 1, 2, and 3 with or without pegfilgrastim in patients with advanced malignancy (NCT01214629).. Molecular Cancer Therapeutics. 10(11_Supplement). A86–A86. 2 indexed citations
15.
Shih, Kuang‐Chung, J. R. Infante, Kyriakos P. Papadopoulos, et al.. (2011). A phase I dose-escalation study of LY2523355, an Eg5 inhibitor, administered either on days 1, 5, and 9; days 1 and 8; or days 1 and 5 with pegfilgrastim (peg) every 21 days (NCT01214642).. Journal of Clinical Oncology. 29(15_suppl). 2600–2600. 3 indexed citations
16.
McGlothlin, Anna, James D. Stamey, & John W. Seaman. (2008). Binary Regression with Misclassified Response and Covariate Subject to Measurement Error: a Bayesian Approach. Biometrical Journal. 50(1). 123–134. 16 indexed citations

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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