Consulting

 

Primary Consulting Experience

 2007 - present Biostatistician / Statistician on various collaborative projects
Statistical consulting (data analysis and manuscript writing) for investigators from FDA, NIH, School of Medicine/UMB, Hilltop of UMBC. Details are listed in the website for "Research" and "Publication".
 2007

 Biostatistician
Center on Aging Studies, Erickson School of Aging Studies, UMBC.- Statistical consulting on policy and socio-cultural aging research for assisted living and long-term care.

 2001 - 2006

 Biostatistics Core Member
Center on Aging and Health, Johns Hopkins University - Statistical consulting (data analysis and manuscript writing) for investigators from JHU and NIA-NIH, under Dr. XianLi Xue and Dr. Karen Bandeen-Roche.

2001 - 2005

 Independent Biostatistical Consultant and Tutor

Client includes faculties from Johns Hopkins school of Nursing, medical staff from Johns Hopkins Hospital and NIH, students from University of Maryland and medical institution of John Hopkins.

Work primarily involve:

  • Develop appropriate statistical analytic plan to address specific scientific aims in which investigators are interested, and present it to them, with a clear statement on how the proposed analysis would succeed.
  • Carry on and fully document analyses, including newly created variables, programs, frequent progress reports, etc.
  • Direct consultation with investigators and collaborators on project progress.
  • Statistical methods primarily focus on: longitudinal data analysis, survival analysis, Generalized linear regression, multivariate methods (e.g latent variable analysis), causal methods.
  • When I was a student at JHU, I had served as an consultant for the thesis projects of Ph.D candiates from other departments for a few years. My goal was to facilitate their learning through the methods that are more appropriate for them. I approach them primarily from three aspects:  
    1. Teach the appropriate methods in a fun and intuitive fashion so that they will get interested in it,  then give guidance on effective literature reviews on related statistical methods.
    2. Open their mind by stimulating their critical thinking ability, e.g. checking model assumptions, and critically examing the study design on how the data get colleced.
    3. Facilate their analysis and teach related computer commands if needed.
Copyright (C) 2005, Yi Huang, Biostatistics Department, Johns Hopkins Bloomberg School of Public Health