American Statistical Association
New York City
Metropolitan Area Chapter

Levin Lecture Series: Fall 2019 Colloquium Seminars
Department of Biostatistics
Mailman School of Public Health
Columbia University



CAPTURING HETEROGENEITY IN
REPEATED MEASURES DATA BY FUSION PENALTY

by

Dr. Lei Liu
Professor
Division of Biostatistics
Washington University School of Medicine in St. Louis

Host: Shaung Wang


Abstract

In this talk we are interested in capturing heterogeneity in clustered or longitudinal data. Traditionally such heterogeneity is modeled by either fixed effects or random effects. In fixed effects models, the number of degrees of freedom for the heterogeneity equals the number of clusters/subjects minus 1, which could result in less efficiency. In random effects models, the heterogeneity across different clusters/subjects is described by e.g., a random intercept with 1 parameter (for the variance of random intercept), which could lead to over simplification and biases (shrinkage estimates). Our "fusion effects" model stands in between these two approaches: we assume that there are unknown number of different levels of heterogeneity, and use the fusion penalty approach for estimation and inference. We evaluate and compare the performance of our method to the fixed and random effects models by simulation studies. We apply our method to the Ocular Hypertension Treatment Study (OHTS) to capture the heterogeneity in the progression rate toward primary open-angle glaucoma of left and right eyes of different subjects.


Date: Thursday, November 21, 2019
Time: 11:30 A.M. - 12:30 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
AR Building
8th Floor Auditorium
New York, New York

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