Sue‐Jane Wang

3.1k citations
73 papers · 1.9k indexed · h-index 24

Impact in

Papers in

Sue‐Jane Wang

68 papers receiving 1.8k citations

Peers

Sue‐Jane Wang
Comparison fields: 5 of 121
  • Statistics and Probability 1.4k
  • Management Science and Operations Research 727
  • Statistics, Probability and Uncertainty 297
  • Economics and Econometrics 470
  • Pharmacology 87
Replace Willi Maurer with:
Willi Maurer Switzerland
Lu Cui United States
Joachim Röhmel Germany
Michael Branson Switzerland
Jonathan Denne United States
Lisa V. Hampson United Kingdom
Satrajit Roychoudhury United States
Mark Chang United States
Didier Renard Belgium
C. K. McPherson United Kingdom
Sue‐Jane Wang relative to Willi Maurer Switzerland Willi Maurer's profile →
Citations per field
00.5×1.5×2.0×
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-authorship network

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
#Work
1 20230
2 20182
3 201512
4 20155
5 20136
6 201164
7 201113
8 201020
9 201017
10 201039
11 200934
12 2007180
13 200623
14 200669
15 200583
16 20041
17 200234
18 200145
19 1999371
20 199468

About Sue‐Jane Wang

Sue‐Jane Wang is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty, Management Science and Operations Research, Economics and Econometrics and Computational Theory and Mathematics, having authored 73 papers that have together received 1.9k indexed citations. Recurring topics across this work include Statistical Methods in Clinical Trials (57 papers), Optimal Experimental Design Methods (27 papers), Health Systems, Economic Evaluations, Quality of Life (20 papers), Meta-analysis and systematic reviews (14 papers), Advanced Causal Inference Techniques (10 papers), Gene expression and cancer classification (9 papers), Statistical Methods and Bayesian Inference (8 papers) and Computational Drug Discovery Methods (6 papers). The work is most often cited by research in Statistics and Probability (1.4k citations), Management Science and Operations Research (727 citations), Statistics, Probability and Uncertainty (297 citations), Economics and Econometrics (470 citations) and Pharmacology (87 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, Yang Yang, Yeh‐Fong Chen and Wentian Guo. Their work appears in journals such as Journal of Biopharmaceutical Statistics, Biometrical Journal, Statistics in Biopharmaceutical Research, Pharmaceutical Statistics and Contemporary Clinical Trials.

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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