Online interdisciplinary seminar series on statistical methodology for social and behavior research(April 16)

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.


Date and Time: FRIDAY, 4/16/2021, 12PM



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.

Detailed Connection Information:

Meeting number: 120 026 0002

Password: RMMESTAT

Join by video system

You can also dial and enter your meeting number. Join by phone

Join by phone

+1-415-655-0002 US Toll

Access code: 120 026 0002

For inquiry, please contact Dr. Xiaojing Wang at