Zhaoling Meng

822 total citations
21 papers, 356 citations indexed

About

Zhaoling Meng is a scholar working on Statistics and Probability, Economics and Econometrics and Genetics. According to data from OpenAlex, Zhaoling Meng has authored 21 papers receiving a total of 356 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Statistics and Probability, 7 papers in Economics and Econometrics and 5 papers in Genetics. Recurrent topics in Zhaoling Meng's work include Statistical Methods in Clinical Trials (12 papers), Health Systems, Economic Evaluations, Quality of Life (6 papers) and Advanced Causal Inference Techniques (6 papers). Zhaoling Meng is often cited by papers focused on Statistical Methods in Clinical Trials (12 papers), Health Systems, Economic Evaluations, Quality of Life (6 papers) and Advanced Causal Inference Techniques (6 papers). Zhaoling Meng collaborates with scholars based in United States, France and Austria. Zhaoling Meng's co-authors include Dmitri V. Zaykin, Margaret G. Ehm, Michael J. Wagner, Chun‐Fang Xu, Debashis Ghosh, Ronglai Shen, Arul M. Chinnaiyan, Robert Kringle, Hui Quan and Telba Irony and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Zhaoling Meng

19 papers receiving 344 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zhaoling Meng United States 9 168 131 85 39 23 21 356
Junichi Asano Japan 11 39 0.2× 131 1.0× 62 0.7× 26 0.7× 15 0.7× 26 309
Edward Abrahams United States 6 69 0.4× 74 0.6× 14 0.2× 45 1.2× 8 0.3× 12 319
Gregory McInnes United States 10 193 1.1× 150 1.1× 21 0.2× 17 0.4× 16 0.7× 12 414
Christina Mitropoulou Greece 15 208 1.2× 145 1.1× 32 0.4× 126 3.2× 15 0.7× 42 562
Marisa Papaluca‐Amati Netherlands 10 40 0.2× 53 0.4× 87 1.0× 75 1.9× 6 0.3× 11 395
Sunayan Bandyopadhyay United States 6 59 0.4× 245 1.9× 16 0.2× 13 0.3× 33 1.4× 7 447
Jackson Burton United States 10 30 0.2× 71 0.5× 21 0.2× 14 0.4× 13 0.6× 24 247
Xinnan Niu United States 10 32 0.2× 121 0.9× 13 0.2× 9 0.2× 15 0.7× 24 300
Sharly J. Nass 3 29 0.2× 86 0.7× 30 0.4× 17 0.4× 4 0.2× 4 230
Carl‐Fredrik Burman Sweden 14 33 0.2× 43 0.3× 366 4.3× 171 4.4× 17 0.7× 34 584

Countries citing papers authored by Zhaoling Meng

Since Specialization
Citations

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

Fields of papers citing papers by Zhaoling Meng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhaoling Meng

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

All Works

20 of 20 papers shown
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Balaur, Irina, Hanna Ćwiek‐Kupczyńska, Yojana Gadiya, et al.. (2024). Getting ready for the European Health Data Space (EHDS): IDERHA's plan to align with the latest EHDS requirements for the secondary use of health data. SHILAP Revista de lepidopterología. 4. 160–160. 8 indexed citations
3.
4.
Zhang, Hao, et al.. (2024). Evaluating the robustness of an AI pathfinder application on eligibility criteria in multiple myeloma trials using real-world data and historical trials. Journal of Comparative Effectiveness Research. 13(7). e230164–e230164. 2 indexed citations
5.
Zhu, Rui, Bianca Vora, Sujatha Menon, et al.. (2023). Clinical Pharmacology Applications of Real‐World Data and Real‐World Evidence in Drug Development and Approval–An Industry Perspective. Clinical Pharmacology & Therapeutics. 114(4). 751–767. 18 indexed citations
6.
Ling, Albee Y., Maria E. Montez‐Rath, Paulo Carita, et al.. (2023). An Overview of Current Methods for Real-world Applications to Generalize or Transport Clinical Trial Findings to Target Populations of Interest. Epidemiology. 34(5). 627–636. 8 indexed citations
7.
Ling, Albee Y., et al.. (2022). Transporting observational study results to a target population of interest using inverse odds of participation weighting. PLoS ONE. 17(12). e0278842–e0278842. 4 indexed citations
8.
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Levenson, Mark, Weili He, Li Chen, et al.. (2022). Statistical Consideration for Fit-for-Use Real-World Data to Support Regulatory Decision Making in Drug Development. Statistics in Biopharmaceutical Research. 15(3). 689–696. 4 indexed citations
10.
Ho, Martin, Mark van der Laan, Hana Lee, et al.. (2021). The Current Landscape in Biostatistics of Real-World Data and Evidence: Causal Inference Frameworks for Study Design and Analysis. Statistics in Biopharmaceutical Research. 15(1). 43–56. 27 indexed citations
11.
Kovatchev, Boris, Zhaoling Meng, Anna M. G. Cali, Riccardo Perfetti, & Marc D. Breton. (2020). Low Blood Glucose Index and Hypoglycaemia Risk: Insulin Glargine 300 U/mL Versus Insulin Glargine 100 U/mL in Type 2 Diabetes. Diabetes Therapy. 11(6). 1293–1302. 5 indexed citations
12.
Meng, Zhaoling, et al.. (2015). CYP2D6 phenotype-based dosing of eliglustat. Molecular Genetics and Metabolism. 114(2). S118–S118. 4 indexed citations
13.
Chen, Xun, et al.. (2012). Handling of baseline measurements in the analysis of crossover trials. Statistics in Medicine. 31(17). 1791–1803. 6 indexed citations
14.
Sun, Guowen, Hui Quan, Robert Kringle, & Zhaoling Meng. (2012). Comparison of Statistical Models Adjusting for Baseline in the Analysis of Parallel-Group Thorough QT/QTc Studies. Journal of Biopharmaceutical Statistics. 22(3). 438–462. 7 indexed citations
15.
Meng, Zhaoling, et al.. (2010). Sample Size Calculation for Thorough QT/QTc Study Considering Various Factors Related to Multiple Time Points. Journal of Biopharmaceutical Statistics. 20(3). 580–594. 5 indexed citations
16.
Meng, Zhaoling, Hui Quan, Li Fan, Robert Kringle, & Gordon H. Sun. (2010). Use of the Average Baseline Versus the Time-Matched Baseline in Parallel Group Thorough QT/QTc Studies. Journal of Biopharmaceutical Statistics. 20(3). 665–682. 13 indexed citations
17.
Zaykin, Dmitri V., Zhaoling Meng, & Margaret G. Ehm. (2006). Contrasting Linkage-Disequilibrium Patterns between Cases and Controls as a Novel Association-Mapping Method. The American Journal of Human Genetics. 78(5). 737–746. 69 indexed citations
18.
Shen, Ronglai, Debashis Ghosh, Arul M. Chinnaiyan, & Zhaoling Meng. (2006). Eigengene-based linear discriminant model for tumor classification using gene expression microarray data. Bioinformatics. 22(21). 2635–2642. 32 indexed citations
19.
Zaykin, Dmitri V., Zhaoling Meng, & Sujit K. Ghosh. (2004). Interval estimation of genetic susceptibility for retrospective case-control studies. BMC Genetics. 5(1). 9–9. 7 indexed citations
20.
Meng, Zhaoling, Dmitri V. Zaykin, Chun‐Fang Xu, Michael J. Wagner, & Margaret G. Ehm. (2003). Selection of Genetic Markers for Association Analyses, Using Linkage Disequilibrium and Haplotypes. The American Journal of Human Genetics. 73(1). 115–130. 114 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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