Brad Efron

2.3k total citations · 1 hit paper
6 papers, 1.6k citations indexed

About

Brad Efron is a scholar working on Molecular Biology, Infectious Diseases and Statistics and Probability. According to data from OpenAlex, Brad Efron has authored 6 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Molecular Biology, 2 papers in Infectious Diseases and 2 papers in Statistics and Probability. Recurrent topics in Brad Efron's work include Statistical Methods in Clinical Trials (2 papers), Gene expression and cancer classification (2 papers) and HIV Research and Treatment (1 paper). Brad Efron is often cited by papers focused on Statistical Methods in Clinical Trials (2 papers), Gene expression and cancer classification (2 papers) and HIV Research and Treatment (1 paper). Brad Efron collaborates with scholars based in United States and United Kingdom. Brad Efron's co-authors include Sabine Ehrt, Martin I. Voskuil, Yang Liu, Gregory Dolganov, Carl Nathan, Dirk Schnappinger, Gary K. Schoolnik, Irene M. Monahan, Philip D. Butcher and Joseph A. Mangan and has published in prestigious journals such as The Journal of Experimental Medicine, Journal of the American Statistical Association and Annals of Internal Medicine.

In The Last Decade

Brad Efron

6 papers receiving 1.5k 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.

Transcriptional Adaptation of Mycobacterium tuberculosis within Macrophages

2003 1.1k citations Dirk Schnappinger, Sabine Ehrt et al. The Journal of Experimental Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Brad Efron United States	6	1.1k	720	588	192	173	6	1.6k
Federico Giannoni Italy	28	723 0.7×	654 0.9×	934 1.6×	194 1.0×	148 0.9×	59	1.9k
Juliano Timm United States	15	1.4k 1.3×	1.1k 1.5×	592 1.0×	294 1.5×	253 1.5×	28	2.0k
Paul J. Converse United States	30	1.8k 1.7×	1.6k 2.2×	746 1.3×	482 2.5×	207 1.2×	77	2.6k
Mamoudou Maïga United States	21	758 0.7×	609 0.8×	372 0.6×	203 1.1×	74 0.4×	78	1.2k
N. Kennedy United Kingdom	22	397 0.4×	799 1.1×	391 0.7×	161 0.8×	54 0.3×	48	1.5k
Henry C. Mwandumba United Kingdom	19	738 0.7×	615 0.9×	227 0.4×	143 0.7×	46 0.3×	77	1.3k
Martin Rao Sweden	22	979 0.9×	705 1.0×	586 1.0×	232 1.2×	64 0.4×	48	2.0k
Benjamin G. Schroeder United States	10	974 0.9×	774 1.1×	680 1.2×	134 0.7×	173 1.0×	11	1.6k
Mridula Bose India	20	936 0.9×	780 1.1×	419 0.7×	341 1.8×	136 0.8×	59	1.4k
Dorothy Fallows United States	22	908 0.9×	743 1.0×	373 0.6×	348 1.8×	32 0.2×	36	1.3k

Countries citing papers authored by Brad Efron

Since Specialization

Citations

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

Fields of papers citing papers by Brad Efron

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Brad Efron

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

All Works

Sort: Min cites: Since: Top N: Style:

6 of 6 papers shown

1.

Taylor, Jonathan, Robert Tibshirani, & Brad Efron. (2004). The 'miss rate' for the analysis of gene expression data. Biostatistics. 6(1). 111–117. 51 indexed citations

2.

Schnappinger, Dirk, Sabine Ehrt, Martin I. Voskuil, et al.. (2003). Transcriptional Adaptation of Mycobacterium tuberculosis within Macrophages. The Journal of Experimental Medicine. 198(5). 693–704. 1114 indexed citations breakdown →

3.

Tibshirani, Robert & Brad Efron. (2002). Pre-validation and inference in microarrays. Statistical Applications in Genetics and Molecular Biology. 1(1). Article1–Article1. 96 indexed citations

4.

Moss, R. Lawrence, et al.. (2001). A meta-analysis of peritoneal drainage versus laparotomy for perforated necrotizing enterocolitis. Journal of Pediatric Surgery. 36(8). 1210–1213. 74 indexed citations

5.

Zolopa, Andrew, Robert W. Shafer, Ann L. Warford, et al.. (1999). HIV-1 Genotypic Resistance Patterns Predict Response to saquinavir–ritonavir Therapy in Patients in Whom Previous Protease Inhibitor Therapy Had Failed. Annals of Internal Medicine. 131(11). 813–821. 176 indexed citations

6.

Efron, Brad & Daniel Feldman. (1991). Compliance as an Explanatory Variable in Clinical Trials. Journal of the American Statistical Association. 86(413). 9–9. 40 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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