coe-staff: Feedback Requested for Clinical Trials Candidate Wei Wu

[Jennifer A McGovney]

The Clinical Trials Methodology search committee would like to thank everyone who took time out of their busy schedules to meet with candidate Wei Wu, and/or attend her colloquium.

If you were unable to attend her colloquium, you can view the recording here: https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=989d9c4f-1aa9-4925-ae30-a07bf7d2ee36

Please provide your feedback on the candidate by completing our survey here: https://oregon.qualtrics.com/SE/?SID=SV_6EeREqeyG76TDeZ

The survey will stay open until January 25. I have also attached her CV to this email. Thank you again for your participation in this important process.


From: coe-staff-bounces at lists.uoregon.edu<mailto:coe-staff-bounces at lists.uoregon.edu> [mailto:coe-staff-bounces at lists.uoregon.edu] On Behalf Of Maggie N Bosworth
Sent: Monday, January 09, 2017 9:22 AM
To: coe-staff at lists.uoregon.edu<mailto:coe-staff at lists.uoregon.edu>; coe-gr at lists.uoregon.edu<mailto:coe-gr at lists.uoregon.edu>
Subject: coe-staff: Colloquium at 11 a.m. - Two Recent Advances of Longitudinal Mediation Analysis with Cross-Lagged Panel Models: Modeling Random Intercept and Random Effects

 Wei Wu, PhD
Two Recent Advances of Longitudinal Mediation Analysis with Cross-Lagged Panel Models: Modeling Random Intercept and Random Effects

Colloquium and Q&A
 Monday, January 9, 2017 - 11:00 AM-12:30 PM
HEDCO 230T
Early Lunch provided at 10:45 AM

Cross-lagged panel models (CLPMs) are widely used to test mediation with longitudinal panel data. Traditional CLPMs assume that intercepts and effects/coefficients in the model are constant/fixed across individuals. This assumption is likely wrong in practice (i.e., the intercepts and effects are varying/random across individuals). In this talk, I will explain the consequences when this assumption is violated, present ways to extend the traditional CLPMs to account for random intercepts and effects, and evaluate the use of these extensions to detect direct and indirect effects in longitudinal mediation analysis using both simulated and empirical data.

Wei Wu, PhD, is an Associate Professor in the Department of Psychology at the University of Kansas. Dr. Wu holds a doctorate in Quantitative Psychology from Arizona State University. Her research focuses on analysis and design issues in longitudinal data analysis, missing data analysis, and structural equation modeling. She has published more than 30 journal articles and book chapters on these topics. Dr. Wu has collaborations with researchers across different disciplines including education, psychology, and social work. She has been a principal investigator on a National Science Foundation grant on planned missing data designs and served as a data analyst and statistical consultant on National Institutes of Health R01 and Patient-Centered Outcomes Research Institute grants. She is currently a consulting editor for Psychological Methods and a panel reviewer for Institute of Education Sciences.




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