Posted on Tuesday, April 6, 2021
This session is jointly sponsored by the Statistics department and the Research
Methods, Measurement, and Evaluation program, University of Connecticut
(UCONN), New England Statistical Society (NESS) and Statistical and Applied
Mathematical Institute (SAMSI) as part of online interdisciplinary seminar
series on statistical methodology for social and behavior research.
Speaker: DR. SUSAN PADDOCK, NORC UNIVERSITY OF CHICAGO
Date and Time: FRIDAY, 4/16/2021, 12PM
Topic: CAUSAL INFERENCE UNDER INTERFERENCE IN DYNAMIC THERAPY GROUP STUDIES
Abstract:
Group therapy is a common treatment modality for behavioral health
conditions. Patients often enter and exit groups on an ongoing basis, leading
to dynamic therapy groups. Examining the effect of high versus low session
attendance on patient outcomes is of interest. However, there are several
challenges to identifying causal effects in this setting, including the lack
of randomization, interference among patients, and the interrelatedness of
patient participation. Dynamic therapy groups motivate a unique causal
inference scenario, as the treatment statuses are completely defined by the
patient attendance record for the therapy session, which is also the
structure inducing interference. We adopt the Rubin Causal Model framework to
define the causal effect of high versus low session attendance of group
therapy at both the individual patient and peer levels. We propose a strategy
to identify individual, peer, and total effects of high attendance versus low
attendance on patient outcomes by the prognostic score stratification. We
examine performance of our approach via simulation, apply it to data from
a group cognitive behavioral therapy trial for reducing depressive symptoms
among patients in a substance use disorders treatment setting, and discuss
the strengths and limitations of this approach.
Bio:
Dr. Susan Paddock is the chief statistician and executive vice president
at NORC at the University of Chicago. She is responsible for the methods of
design and analysis used in NORC proposals and projects and for the NORC
corporate research and development enterprise. Her research includes
developing innovative statistical methods, with a focus on Bayesian methods,
multilevel modeling, nonparametric Bayes, longitudinal data analysis, and
missing data techniques. Dr. Paddock is the principal investigator of a project
sponsored by the National Institute on Alcohol Abuse and Alcoholism to develop
methods for analyzing data arising from studies of group therapy-based
interventions. She was the principal investigator of a project sponsored by
the Agency for Healthcare Research and Quality to improve the science of public
reporting of health care provider performance. She co-led a project to conduct
analyses related to the Medicare Advantage Plan Ratings for Quality Bonus
Payments. Her other substantive research interests include health services
research, substance abuse treatment, quality of health care, and veterans’
health care. Prior to joining NORC, she spent 20 years as a senior statistician
with RAND Corporation. From 2008 to 2013, she led the RAND Statistics Group.
She has served on editorial boards for the Annals of Applied Statistics,
Journal of the American Statistical Association, and Medical Care, and has
served on committees for the American Statistical Association (ASA) and the
National Academies of Sciences, Engineering, and Medicine. She is a fellow of
the ASA and was the recipient of the 2013 Mid-career Award of the Health
Policy Statistics Section of the ASA. She received her PhD in statistics from
Duke University and her BA in mathematics and biostatistics from the University
of Minnesota.
For inquiry, please contact Dr. Xiaojing Wang at xiaojing.wang@uconn.edu
