The College of Health Sciences Biostatistics Core Facility provides statistical support to faculty and student investigators doing basic, translational, and clinical research. The facility’s primary roles are to provide methodological and statistical expertise for grant proposals and to help perform analyses for manuscripts. Additionally, the core facility is able to assist with complex dataset planning, data integration, and custom software solutions.
Our goal is to establish long standing relationships, becoming collaborators with the Principal Investigators within the College.
- Experimental design
- Statistical analysis plans
- Power and sample size calculations
- Selection and evaluation of instruments and surveys
- Statistical analyses, and aid in communicating findings
- Dataset planning, integration, and cleaning
- Data exploration and visualization
- Application development
- Additionally, the Core Facility hosts speakers relevant to current research in the College as well as holds seminars designed to improve researchers’ skills.
Contact & Scheduling Timeline
If you would like to use these services you may either contact whomever you have a relationship directly or email Ryan Pohlig.
Initially, an overview meeting will be scheduled to assess your needs, and to discuss the project and the research design. This may or may not be the person you end up working on a project with, as work will be balanced based on expertise and current workload.
In order to better manage work on projects with external deadlines, we request that you
- Contact us at least ONE month before the Grant Proposal is due to the Central Research Office to discuss the scope of the work and develop an appropriate timeline.
- For all other work, TWO weeks’ notice is needed to facilitate workload and scheduling.
Our expectations, researchers who follow the guidelines will be prioritized.
- Reasonable salary support will be included in grants to account for our efforts in study planning and for future analyses to be conducted when the research is finished.
- Proper authorship credit on manuscripts for data analysis, writing of the statistical methods and results sections, and/or review of entire manuscript.
- Addressing shortcomings in data management, such as data cleaning and preparation, is not a priority and will necessarily slow our response to you. It is expected that researchers provide data (wide or long format) using the dataset management guidelines outlined here and include a codebook/data dictionary.
In general priorities for statistical work, from highest to lowest are
- Preparation of Grant Proposals with Salary Support
- Work that is supported on a currently funded Grant
- Preparation of Grant Proposals without Salary Support
- Analysis of Pilot data for future Grant proposals
- Unfunded Research projects
For Our Student and Post-Doc Collaborators
As time permits, we are able to assist students and post-docs in CHS with:
- Preparation of grant proposals
- Statistical analyses for manuscripts
- Serving on PhD committees
It is expected that the same guidelines, outlined above, are to be followed (i.e. salary support on grants, proper authorship credit, etc.). We are able to mentor students in using methodologies that their Advisor may be unfamiliar with, a role that is common for committee members.
To request our help, the initial contact email must include your Advisor. Subsequent meetings may also require your Advisor’s attendance.
Ryan T. Pohlig, Ph.D.
Sr. Biostatistician & Manager of Biostatistical Core
His interests include Structural Equation Modeling, Mixed Modeling, Mediation and Moderation Analyses, and Study Design.
Barry A. Bodt, Ph.D.
His interests include Experimental Design, Data Mining, Classification and Regression Trees, and Cluster Analysis.
Melissa L. Ziegler, Ph.D.
Her research interests include Mixed Modeling, Survival Analysis, Time Series Data, and Generalized Linear Modeling.
Bill Flynn, M.P.H.
His research interests include building custom software solutions, data integration, and data analytics.
- UCLA’s Software Guide
- Reliability Measures for Clinical Researchers
- Russ Lavery’s Statistics Resources
- Sample size estimation and power analysis for clinical research studies
- Ethical Treatment of Data
- Data Transformations, using Box-Cox
SPSS & SAS
SPSS and SAS Macros and Syntax: