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    Last Name

    Hua Fang PhD

    TitleAssociate Professor
    InstitutionUniversity of Massachusetts Medical School
    DepartmentQuantitative Health Sciences
    AddressUniversity of Massachusetts Medical School
    55 Lake Avenue North
    Worcester MA 01655
      Other Positions
      InstitutionUMMS - Graduate School of Biomedical Sciences
      DepartmentClinical Population Health Research

      InstitutionUMMS - Graduate School of Biomedical Sciences
      DepartmentMasters in Clinical Investigation



        Dr. Fang is Associate Professor in Division of Biostatistics and Health Services Research, Department of Quantitative Health Science. Before joining UMass Medical School in 2010, she had served as Research Assistant Professor/ Biostatistician in University of Nebraska-Lincoln for four years. She graduated from Ohio University in 2006 with her Ph.D. core in Statistics. 

        She won a paper award at the 2006 Joint Research Conference on Statistics in quality industry and technology. She won Layman Awards for missing data modeling and growth trajectory pattern recognition via UNL research council competition in 2008.

        She is a recipient of the 2012 UMass CTSA Pilot Project award for modeling heterogeneity of treatment effects (HTE) in longitudinal RCT and observational studies, including 3 RCTs and 2 observational studies for comparative effectiveness research.

        She is the PI of NIH/NIDA R01 DISC project (2013-2016): Develop behavioral trajectory pattern recognition methods and tools for large-scale unstructured data from longitudinal RCT behavioral intervention studies for smoking cessation, generally, substance use, such as internet-delivered RCT interventions, and small-scaled culturally-tailored cognitive interventions. Her methods are used to capture behavioral (e.g, engagment/response) changes, identify and validate patterns, inform which  components or measures are working for which patients/users at what time and to what degree, therefore to clarify the efficacy of a trial and the effectiveness of a treatment/exposure. This approach is expanding to various application areas.

        Dr. Fang has been a statistical consultant in health, medicine, economics, and bio-engineering areas for years. She also participated in large-scale multi-disciplinary projects at both state and federal levels. She is PI/Co-I/Statistician on several extramural grants: NIH, VA or PCORI.  

        Dr. Fang's research interests include computational statistics, behavioral trajectory pattern recogntion, research design, statistical modeling and analyses in clinical and translational research. She is interested in developing novel methods and applying emerging robust techniques to enable or improve the health studies that can have potential impact on the treatment or prevention of human diseases. Her research applications are in data science, substance use, infectious diseases, immunology, nutritional epidemiology, behavioral medicine, and E-/M-health.

        Computational Statistics and Data Science (CSDS) lab --- PI: Julia Hua Fang


        See more selected Publications on E-scholarship

        selected publications
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        1. Ash AS, Kroll-Desrosiers AR, Hoaglin DC, Christensen K, Fang H, Perls TT. Are Members of Long-Lived Families Healthier Than Their Equally Long-Lived Peers? Evidence From the Long Life Family Study. J Gerontol A Biol Sci Med Sci. 2015 Aug; 70(8):971-6.
          View in: PubMed
        2. Robinson WP, Huang W, Rosen A, Schanzer A, Fang H, Anderson FA, Messina LM. The Agency for Healthcare Research and Quality Inpatient Quality Indicator #11 overall mortality rate does not accurately assess mortality risk after abdominal aortic aneurysm repair. J Vasc Surg. 2015 Jan; 61(1):44-9.
          View in: PubMed
        3. Nelson JM, Choi HJ, Clark CA, James TD, Fang H, Wiebe SA, Espy KA. Sociodemographic risk and early environmental factors that contribute to resilience in executive control: A factor mixture model of 3-year-olds. Child Neuropsychol. 2015 May; 21(3):354-78.
          View in: PubMed
        4. Wiebe SA, Fang H, Johnson C, James KE, Espy KA. Determining the impact of prenatal tobacco exposure on self-regulation at 6 months. Dev Psychol. 2014 Jun; 50(6):1746-56.
          View in: PubMed
        5. Fang H, Dukic V, Pickett KE, Wakschlag L, Espy KA. Detecting graded exposure effects: a report on an East Boston pregnancy cohort. Nicotine Tob Res. 2012 Sep; 14(9):1115-20.
          View in: PubMed
        6. Fang H, Johnson C, Stopp C, Espy KA. A new look at quantifying tobacco exposure during pregnancy using fuzzy clustering. Neurotoxicol Teratol. 2011 Jan-Feb; 33(1):155-65.
          View in: PubMed
        7. Fang H, Johnson C, Chevalier N, Stopp C, Wiebe S, Wakschlag LS, Espy KA. Using propensity score modeling to minimize the influence of confounding risks related to prenatal tobacco exposure. Nicotine Tob Res. 2010 Dec; 12(12):1211-9.
          View in: PubMed
        8. Fang H, Rizzo ML, Wang H, Espy KA, Wang Z. A new nonlinear classifier with a penalized signed fuzzy measure using effective genetic algorithm. Pattern Recognit. 2010; 43(4):1393-1401.
          View in: PubMed
        9. Fang H, Espy KA, Rizzo ML, Stopp C, Wiebe SA, Stroup WW. Pattern Recognition of Longitudinal Trial Data with Nonignorable Missingness: An Empirical Case Study. Int J Inf Technol Decis Mak. 2009 Sep 1; 8(3):491-513.
          View in: PubMed
        10. Espy KA, Fang H, Charak D, Minich N, Taylor HG. Growth mixture modeling of academic achievement in children of varying birth weight risk. Neuropsychology. 2009 Jul; 23(4):460-74.
          View in: PubMed
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