Faculty Directory Listing

Susan G. Silva
Tel: (919) 681-3004
Office: 3136 Pearson Building
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Susan G. Silva, PhD

Associate Professor

  • Brief Bio

    Dr. Silva is Research Associate Professor and Statistician in the Office of Research Affairs at the Duke University School of Nursing. She also has appointments in the Department of Psychiatry and Behavioral Sciences of the Duke University School of Medicine and in the Department of Clinical Trials Statistics of the Duke Clinical Research Institute.

    Dr. Silva has extensive formal training in both biostatistics and behavioral statistics, and has taught statistics courses designed for psychology students and neuropsychiatry research fellows. She is a senior statistician on the NIMH Data Safety and Monitoring Board (DSMB) as well as a statistical reviewer for several industry-sponsored DSMBs.

    Dr. Silva earned her PhD from the Experimental Psychology-Cognitive Neuroscience Program at North Carolina State University, and completed a NIH-funded post-doctoral fellowship in Neuropsychology and Cognitive Neuroscience at the Brain and Development Research Center at the University of North Carolina at Chapel Hill (UNC-CH). Before coming to Duke in 1999, Dr. Silva was a member of the faculty in the Department of Psychiatry at the UNC-CH School of Medicine, where she served as the Director of the Neurobehavioral Assessment Core and the Associate Director of the Data Management and Biostatistics Core for their NIMH-sponsored Mental Health and Neurosciences Clinical Research Center.

    Academic Program Affiliations

    PhD in Nursing Program
    Doctor of Nursing Practice Program

    Education

    PhDNorth Carolina State University
    MAEast Carolina University
    BAEast Carolina University

    Research Interests

    Dr. Silva currently serves as the Statistical PI for the Substance Use Outcomes Following Treatment for Adolescent Depression (SOFTAD) study. She is also a member of the NIMH Interventions Committee for Disorders Involving Children and their Families (ITVC).

    Her statistical expertise includes the: (1) design of longitudinal studies; (2) application of hierarchical or mixed models; (3) analysis of moderator and mediator variables; (4) use of Generalized Estimating Equations; (5) assessment of reliability and validity; and (5) development and implementation of randomization schemes.