Nancy L. Geller
About
Nancy L. Geller is a scholar working on Surgery, Statistics and Probability and Cardiology and Cardiovascular Medicine.
According to data from OpenAlex, Nancy L. Geller has authored 220 papers receiving a total of 11.5k indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Surgery, 53 papers in Statistics and Probability and 35 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Nancy L. Geller's work include Statistical Methods in Clinical Trials (47 papers), Testicular diseases and treatments (28 papers) and Health Systems, Economic Evaluations, Quality of Life (26 papers). Nancy L. Geller is often cited by papers focused on Statistical Methods in Clinical Trials (47 papers), Testicular diseases and treatments (28 papers) and Health Systems, Economic Evaluations, Quality of Life (26 papers). Nancy L. Geller collaborates with scholars based in United States, Canada and United Kingdom. Nancy L. Geller's co-authors include Howard I. Scher, George J. Bosl, Pramod C. Sogani, Alan Yagoda, Willet F. Whitmore, Harry W. Herr, Michael J. Morse, Anastasios A. Tsiatis, Robin C. Watson and William R. Fair and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and The Lancet.
In The Last Decade
Nancy L. Geller
216 papers receiving 10.9k citations
Hit Papers
Author Peers
Peers are selected by citation overlap in the author's most active subfields. citations · hero ref
| Author | Last Decade | Papers | Cites | |||||
|---|---|---|---|---|---|---|---|---|
| Nancy L. Geller | 4.9k | 2.1k | 2.1k | 1.6k | 1.5k | 220 | 11.5k | |
| M. C. Pike | 1.8k 0.4× | 408 0.2× | 2.9k 1.4× | 2.2k 1.4× | 864 0.6× | 33 | 10.0k | |
| E. A. Gehan | 1.3k 0.3× | 619 0.3× | 2.4k 1.2× | 2.6k 1.6× | 1.1k 0.7× | 56 | 8.2k | |
| Anna E. Barón | 1.6k 0.3× | 1.1k 0.5× | 3.6k 1.7× | 3.7k 2.3× | 795 0.5× | 231 | 12.8k | |
| Edmund A. Gehan | 1.5k 0.3× | 463 0.2× | 3.2k 1.6× | 4.1k 2.6× | 999 0.7× | 143 | 10.2k | |
| Hajime Uno | 1.0k 0.2× | 1.5k 0.7× | 2.0k 1.0× | 1.3k 0.8× | 742 0.5× | 189 | 8.5k | |
| Ulrich Mansmann | 1.6k 0.3× | 596 0.3× | 3.0k 1.5× | 2.0k 1.3× | 450 0.3× | 362 | 11.4k | |
| Theodore Karrison | 2.1k 0.4× | 415 0.2× | 3.8k 1.9× | 3.0k 1.9× | 870 0.6× | 322 | 14.5k | |
| Matthew R. Sydes | 3.0k 0.6× | 578 0.3× | 4.0k 1.9× | 7.9k 4.9× | 524 0.3× | 234 | 15.2k | |
| Madhu Mazumdar | 7.3k 1.5× | 1.2k 0.6× | 4.4k 2.1× | 7.4k 4.6× | 504 0.3× | 275 | 17.5k | |
| Limin X. Clegg | 2.7k 0.6× | 1.4k 0.7× | 6.6k 3.2× | 3.7k 2.3× | 484 0.3× | 44 | 16.7k |
Countries citing papers authored by Nancy L. Geller
Since
Specialization
Citations
This map shows the geographic impact of Nancy L. Geller'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 Nancy L. Geller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nancy L. Geller more than expected).
Fields of papers citing papers by Nancy L. Geller
Since
Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences
This network shows the impact of papers produced by Nancy L. Geller. 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 Nancy L. Geller. The network helps show where Nancy L. Geller may publish in the future.
Co-authorship network of co-authors of Nancy L. Geller
This figure shows the co-authorship network connecting the top 25 collaborators of Nancy L. Geller. A scholar is included among the top collaborators of Nancy L. Geller 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 Nancy L. Geller. Nancy L. Geller is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
1.
Lindsell, Christopher J., Matthew S. Shotwell, Kevin J. Anstrom, et al.. (2024). The statistical design and analysis of pandemic platform trials: Implications for the future. Journal of Clinical and Translational Science. 8(1). e155–e155.
2.
Uschner, Diane, Oleksandr Sverdlov, Jonathan Chipman, et al.. (2024). Rejoinder. Statistics in Biopharmaceutical Research. 16(4). 425–427.
3.
Martens, Michael J., et al.. (2024). Sequential monitoring of time-to-event safety endpoints in clinical trials. Clinical Trials. 22(3). 267–278.
4.
Uschner, Diane, Oleksandr Sverdlov, Jonathan Chipman, et al.. (2023). Using Randomization Tests to Address Disruptions in Clinical Trials: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions. Statistics in Biopharmaceutical Research. 16(4). 405–413.
5.
Bashir, Qaiser, Taiga Nishihori, Marcelo C. Pasquini, et al.. (2022). A Multicenter Phase II, Double-Blind, Placebo-Controlled Trial of Maintenance Ixazomib After Allogeneic Transplantation for High-Risk Multiple Myeloma: Results of the Blood and Marrow Transplant Clinical Trials Network 1302 Trial. Transplantation and Cellular Therapy. 29(6). 358.e1–358.e7.
6.
Childs, Richard, Xin Tian, Enkhtsetseg Purev, et al.. (2021). Combined haploidentical and cord blood transplantation for refractory severe aplastic anaemia and hypoplastic myelodysplastic syndrome. British Journal of Haematology. 193(5). 951–960.
7.
Pereira, Naveen L., Charanjit S. Rihal, Ryan J. Lennon, et al.. (2021). Effect of CYP2C19 Genotype on Ischemic Outcomes During Oral P2Y12 Inhibitor Therapy. JACC: Cardiovascular Interventions. 14(7). 739–750.
8.
Ahmad, Tariq, Mona Fiuzat, Michael Pencina, et al.. (2014). Charting a Roadmap for Heart Failure Biomarker Studies. JACC Heart Failure. 2(5). 477–488.
9.
Kernan, Nancy A., John E. Wagner, L.A. Baxter-Lowe, et al.. (2006). Umbilical cord blood transplantation in pediatric patients: Results of the prospective, multi-institutional Cord Blood Transplantation Study (COBLT). Biology of Blood and Marrow Transplantation. 12(2). 14–15.
10.
Ingkanisorn, W. Patricia, Raymond Y. Kwong, Nancy L. Geller, et al.. (2006). Prognosis of Negative Adenosine Stress Magnetic Resonance in Patients Presenting to an Emergency Department With Chest Pain. Journal of the American College of Cardiology. 47(7). 1427–1432.
11.
Bradstock, K. F., David Gottlieb, Didier Blaise, et al.. (2002). Non-myeloablative allogeneic stem cell transplantation (NST) for metastatic melanoma: nondurable chemotherapy responses without clinically meaningful graft-vs-tumor (GVT) effects.. Blood. 100(11).
12.
Domanski, Michaël, Craig B. Borkowf, Lucien Campeau, et al.. (2000). Prognostic factors for atherosclerosis progression in saphenous vein grafts. Journal of the American College of Cardiology. 36(6). 1877–1883.
13.
Domanski, Michaël, Derek V. Exner, Craig B. Borkowf, et al.. (1999). Effect of angiotensin converting enzyme inhibition on sudden cardiac death in patients following acute myocardial infarction. Journal of the American College of Cardiology. 33(3). 598–604.
14.
Buyse, Marc, Stephen L. George, Stephen Evans, et al.. (1999). The role of biostatistics in the prevention, detection and treatment of fraud in clinical trials. Statistics in Medicine. 18(24). 3435–3451.
15.
Campeau, Lucien, Genell L. Knatterud, Mikołaj Domański, et al.. (1998). Delayed progression of atherosclerosis in coronary bypass grafts is similar in women compared to men following aggressive cholesterol lowering despite more frequent risk factors: post CABG trial. Journal of the American College of Cardiology. 31. 280–281.
16.
Pepine, Carl J., Thomas Andrews, Sandra Forman, et al.. (1997). Relation Between Clinical, Angiographic and Ischemic Findings at Baseline and Ischemia-Related Adverse Outcomes at 1 Year in the Asymptomatic Cardiac Ischemia Pilot Study fn1fn1This study was funded by the National Heart, Lung, and Blood Institute, Cardiac Diseases Branch, Division of Heart and Vascular Diseases, National Institutes of Health, Bethesda, Maryland, by Research Contracts HV-90-07, HV90-08, HV-91-05 to HV-91-14. Study medications and placebo were donated by Zeneca Pharmaceuticals Group, Wilmington, Delaware; Marion-Merrell Dow, Kansas City, Missouri; and Pfizer, New York, New York. Support for electrocardiographic data collection was provided in part by Applied Cardiac Systems, Laguna Hills, California; Marquette Electronics, Milwaukee, Wisconsin; Mortara Instrument, Milwaukee, Wisconsin; and Quinton Instruments, Seattle, Washington. Some centers had partial support from grants from the General Clinical Research Center, National Institutes of Health.. Journal of the American College of Cardiology. 29(7). 1483–1489.
17.
Rogers, W J, Martial G. Bourassa, Thomas Andrews, et al.. (1995). Asymptomatic cardiac ischemia pilot (ACIP) study: Outcome at 1 year for patients with asymptomatic cardiac ischemia randomized to medical therapy or revascularization. Journal of the American College of Cardiology. 26(3). 594–605.
18.
Pepine, Carl J., Nancy L. Geller, Genell L. Knatterud, et al.. (1994). The Asymptomatic Cardiac Ischemia Pilot (ACIP) study: Design of a randomized clinical trial, baseline data and implications for a long-term outcome trial. Journal of the American College of Cardiology. 24(1). 1–10.
19.
Knatterud, Genell L., Martial G. Bourassa, Carl J. Pepine, et al.. (1994). Effects of treatment strategies to suppress ischemia in patients with coronary artery disease: 12-Week results of the Asymptomatic Cardiac Ischemia Pilot (ACIP) study. Journal of the American College of Cardiology. 24(1). 11–20.
20.
Dmitrovsky, Ethan, Victor E. Reuter, Felipe Samaniego, et al.. (1989). Isochromosome of Chromosome 12: Clinically Useful Marker for Male Germ Cell Tumors. JNCI Journal of the National Cancer Institute. 81(24). 1874–1878.
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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