The 3rd Statistical Consulting Services (SCS)
Workshop Day Presentations
Thursday, May 10, 2018
University of Connecticut, Storrs, CT
https://stat.uconn.edu/workshops/
The Statistical Consulting Service (SCS) at the University of Connecticut is pleased to
announce a series of four workshops on Thursday, May 10, 2018, covering a 10-year progress
report from SCS (2008-2018) and a SCS case study, methods and tools for exploratory data
analysis with R, analysis of patient-reported outcomes, and incorporating statistics into
research grants. The livestream of this event will be available and the link will be posted on
https://stat.uconn.edu/ in late April.
Location: Laurel Hall (LH) 101
Date: Thursday, May 10, 2018
Sessions:
1. A 10-Year Progress Report from SCS (2008-2018) and a SCS Case Study
2. Methods and Tools for Exploratory Data Analysis with R
3. Analysis of Patient-Reported Outcomes
4. Incorporating Statistics into Research Grants
Schedule:
Thursday, May 10, 2018
8:45 AM - 12:00 PM Sign-in
9:15 AM -10:15 AM Session 1
10:15 AM- 10:30 AM Break
10:30 AM - 12:00 PM Session 2
12:00 PM - 1:15 PM Lunch
1:15 PM - 2:45 PM Session 3
2:45 PM - 3:00 PM Break
3:00 PM - 4:30 PM Session 4
Who: Any UConn or UCHC faculty, post-doc, graduate students, and undergraduate stu-
dents.
Registration: http://merlot.stat.uconn.edu/www/consulting/workshops2/register.php
Registration is now open and there is no registration fee. Participants can sign up for one
or multiple sessions. Lunch will be provided to all participants in the Union Street Market
(USM). Please pick up lunch cards upon sign-in on May 10. Registration will be closed when
the cap number (200 for each session) is reached. For more information regarding the work-
shops, please contact the SCS workshops coordinator, Chen Zhang (chen.zhang@uconn.edu).
Sessions for the 3rd SCS Workshop Day
Presentations
Thursday, May 10, 2018
Laurel Hall (LH) 101
https://stat.uconn.edu/workshops/
Session 1: A 10-Year Progress Report from SCS (2008-2018) and
a SCS Case Study
Presenters:
Sarah Crothers is a senior undergraduate student at UConn major-
ing in statistics and minoring in business. She has been working for
Statistical Consulting Services as an administrative specialist for two
years. Sarah is graduating with honors recognition in May 2018 after
completing her honors thesis on database building and augmentation.
After graduation, she will be working for the Hartford in a technology
and data rotational program.
Henry Linder is a PhD student in the Department of Statistics. His
research interest is in applied statistics, particularly for large, multi-
variate datasets.
Outline: Sarah will provide an in-depth and comprehensive report of the consulting ser-
vices the SCS has provided during the last 10 years. Henry will present the statistical meth-
ods, interactive tools, and data visualization the consulting team has developed/created for
an ongoing consulting project with the University's o
ce for Utility Operations and Energy
Management.
Session 2: Methods and Tools for Exploratory Data Analysis with
R
Presenters:
Yan Zhuang is a Ph.D. student in the Department of Statistics at
University of Connecticut, under the supervision of Professor Nitis
Mukhopadhyay. Her research has been mainly focused on Sequential
Analysis, Statistical Inference, and Sampling Strategies. In this Fall,
she will begin her Assistant Professor position at Connecticut College,
in New London, CT.
Chen Zhang is completing his fourth year as a Ph.D. student in the
Department of Statistics at the University of Connecticut. He has
been on the SCS consulting team since August 2016, and he is also
the instructor of STAT 2215Q Introduction to Statistics II. Chen has
been doing research under the guidance of Dr. Nitis Mukhopadhyay
on sequential experimental designs for statistical inference on big data
problems.
Outline: Exploratory data analysis (EDA) is a useful and e
ective approach to analyzing
data to summarize their main characteristics, often with visualizations. The goal of EDA
is to explore and understand the data, possibly formulating hypotheses that could lead to
new data collection and experiments. In this workshop, we will provide an overview of
methods and tools for EDA with R. Participants are encouraged to bring their laptops and
follow along. R and RStudio can be downloaded for free at: https://cran.r-project.org and
https://www.rstudio.com/products/rstudio/download/.
Prerequisite: Some beginner-level coding experience with R is recommended but not re-
quired.
Session 3: Analysis of Patient-Reported Outcomes
Presenter:
Dr. Joseph C. Cappelleri is an executive director of biostatistics in the
Statistical Research and Data Science Center at Pfi
zer Inc. He earned
his M.S. in statistics from the City University of New York, Ph.D.
in psychometrics from Cornell University, and M.P.H. in epidemiology
from Harvard University. As an adjunct professor, Dr. Cappelleri has
served on the faculties of Brown University, University of Connecticut,
and Tufts Medical Center.
He has co-authored approximately 900 external presentations and 450 publications (includ-
ing three books) on clinical and methodological topics including on regression-discontinuity
designs, meta-analyses, and health measurement scales. Dr. Cappelleri is lead author of the
monograph Patient-Reported Outcomes: Measurement, Implementation and Interpretation.
He is a Fellow of the American Statistical Association.
Outline: Patient-reported outcomes are often relevant in studying a variety of diseases
and outcomes that cannot be assessed adequately without a patients evaluation and whose
key questions require patients input on the impact of a disease or a treatment. To be
useful to patients, researchers and decision makers, a patient-reported outcome (PRO) must
undergo a validation process to support that it measures what it is intended to measure
accurately and reliably. In this workshop, after presentation of some key elements on the
development of a PRO measure, the core topics of validity and reliability of a PRO measure
will be discussed. Exploratory and con
rmatory factor analyses, techniques to understand
the underlying structure of a PRO measure, will be described. The topic of mediation
modeling will be presented as a way to identify and explain the mechanism that underlies
an observed relationship between an independent variable and a dependent variable via
the inclusion of a third variable called the mediator variable. Also discussed will be item
response theory and, time permitting, longitudinal analysis. Illustrations will be provided
mainly through real-life and simulated examples.
Reference: Cappelleri JC, Zou KH, Bushmakin AG, Alvir JMJ, Alemayehu D, Symonds
T. Patient-Reported Outcomes: Measurement, Implementation and Interpretation. Boca
Raton, Florida: Chapman & Hall/CRC Press. 2013.
Session 4: Incorporating Statistics into Research Grants
Presenter:
Dr. James Grady received his doctoral degree in Biostatistics from
the University of North Carolina, Chapel Hill in 1992 and joined the
University of Texas Medical Branch faculty in 1993. He also has an
MPH from Yale and went to Fordham University in New York City for
undergraduate. He was a Professor in the Department of Preventive
Medicine and Community Health until 2010. He is currently director of
the Biostatistics Center for the Connecticut Institute for Clinical and
Translational Science (CICATS) at the University of Connecticut and
Professor in the School of Medicine.
He has many years of research experience as the lead biostatistician for numerous NIH-funded
collaborative studies involving clinical and translational science in large scale population
based studies and basic science. He was a GCRC and CTSA biostatistician for more than
15 years at UTMB. He is a regular grant reviewer for NIDCR. He is past president of the
Association of Clinical and Translational Statisticians.
Outline: This presentation will cover the statistical components of research grants typi-
cally required for successful applications. Topics will include a review of study types and
their statistical characteristics, formulation of speci
c aims and hypotheses, development of
a statistical plan for your research grant, review of sample size and power and practical ad-
vice on how to justify your sample size. This will be a non-technical session geared towards
research scientists who prepare grants and applied statisticians involved in collaborative
studies.
For more information, contact: Tracy Burke at tracy.burke@uconn.edu