Sue‐Jane Wang

3.2k citations
76 papers · 2.1k · h-index 25

Impact in

Papers in

Sue‐Jane Wang

70 papers receiving 1.9k citations

Peers

Sue‐Jane Wang
Comparison fields: 5 of 111
  • Statistics and Probability 1.5k
  • Management Science and Operations Research 749
  • Statistics, Probability and Uncertainty 288
  • Economics and Econometrics 481
  • Urology 63
Replace Willi Maurer with:
Willi Maurer Switzerland
Michael Branson Switzerland
Jonathan Denne United States
Joachim Röhmel Germany
Satrajit Roychoudhury United States
Lisa V. Hampson United Kingdom
Mark Chang United States
Didier Renard Belgium
Marc Vandemeulebroecke Switzerland
Alexei Dmitrienko United States
Sue‐Jane Wang relative to Willi Maurer Switzerland Willi Maurer's profile →
Citations per field
00.5×10×20×31.5×
Willi Maurer · 1×
Citations per year

Countries citing papers authored by Sue‐Jane Wang

Since Specialization
Citations

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

Fields of papers citing papers by Sue‐Jane Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sue‐Jane Wang. 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 Sue‐Jane Wang. The network helps show where Sue‐Jane Wang may publish in the future.

Co-authors

The 25 scholars most cited alongside Sue‐Jane Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sue‐Jane Wang Line = papers co-authored together Sue‐Jane Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 76 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1999403
2 2007195
3 2002152
4 201790
5 200587
6 200673
7 199471
8 201168
9 200260
10 200758
11 200651
12 200650
13 200150
14 201043
15 200935
16 200235
17 200330
18 200228
19 201028
20 201027

About Sue‐Jane Wang

Sue‐Jane Wang is a scholar working on Statistics and Probability, Management Science and Operations Research, Economics and Econometrics, Statistics, Probability and Uncertainty and Molecular Biology, having authored 76 papers that have together received 2.1k indexed citations. Recurring topics across this work include Statistical Methods in Clinical Trials (58 papers), Optimal Experimental Design Methods (27 papers), Health Systems, Economic Evaluations, Quality of Life (21 papers), Meta-analysis and systematic reviews (14 papers), Gene expression and cancer classification (9 papers), Advanced Causal Inference Techniques (8 papers), Statistical Methods and Bayesian Inference (7 papers) and Computational Drug Discovery Methods (6 papers). The work is most often cited by research in Statistics and Probability (1.5k citations), Management Science and Operations Research (749 citations), Statistics, Probability and Uncertainty (288 citations), Economics and Econometrics (481 citations) and Urology (63 citations). Sue‐Jane Wang has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Hung Hung, Robert T. O’Neill, Lu Cui, H. M. James Hung, Yi Tsong, John Lawrence, Yuan Ji, Henry Lynn, Wentian Guo and Shengjie Yang. Their work appears in journals such as Biometrical Journal, Journal of Biopharmaceutical Statistics, Statistics in Biopharmaceutical Research, Pharmaceutical Statistics and Statistics in Medicine.

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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