qing wu

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Qing Wu, M.D., Sc.D.


Epidemiology and Biostatistics
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Qing Wu is an associate professor of biostatistics in the Department of Epidemiology and Biostatistics, School of the Public Health and a founding member of the Nevada Institute of Personalized Medicine, UNLV.

Dr. Wu’s research interests include the development and validation of personalized clinical normative values using modern statistical methodology and existing big data, meta-analyses of epidemiologic studies and clinical trials, genome-wide meta-analysis and mega-analysis, statistical methodology research in meta-analysis, clinical trials and big data analysis, and study design and statistical methodology development in observational studies and clinical trials. Dr. Wu has extensive experience in multidisciplinary collaborative research and statistical consulting in biomedical research. His collaborative works have led to 45 research grants funded by federal agencies, major industries and research foundations, on which he served as co-investigator or co-principle investigator and lead statistician. Dr. Wu is also an affiliate faculty member of biomedical informatics at the College of Health Solutions, Arizona State University, as well as an academic editor of PLOS ONE, for which he serves on both the statistics advisory and editorial boards. He has also served as a peer reviewer for numerous journals.

Dr. Wu’s areas of expertise include statistical modeling and model validation, research design, sample size and power analysis, statistical consulting for grant proposals, clinical trials, survival analysis, meta-analysis, and statistical reporting. His is also an expert in observational clinical research, outcomes research, personalized medicine research, technology evaluation, cancer research and osteoporosis research.

Research Specialties

  • Meta-analysis, genome-wide meta-analysis and mega-analysis
  • Statistical modeling and model validation for clinical application
  • Research design and methodology in clinical trials and epidemiological studies
  • Methodology development for personalized diagnosis
  • Statistical consulting in bio-medical research