Abstract Details
Activity Number:
|
435
|
Type:
|
Invited
|
Date/Time:
|
Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Korean International Statistical Society
|
Abstract #310789
|
|
Title:
|
Subgroup Identification for Longitudinal Data with Unspecified Random Effects
|
Author(s):
|
Hyunkeun Cho*+ and Annie Qu and Peng Wang
|
Companies:
|
Western Michigan University and University of Illinois at Urbana-Champaign and Bowling Green State University
|
Keywords:
|
Classification tree ;
Penalized quasilikelihood ;
Quadratic inference function ;
Personalized treatment ;
Random-effects model ;
Subgroup identification
|
Abstract:
|
We develop new modeling and estimation for personalized treatment for individuals with high heterogeneity. Incorporating subject-specific information into treatment subgroup is critical since individuals could react to the same treatment quite differently. We estimate unobserved individual treatment effects through the conditional random-effects modeling, and identify the optimal treatment for individuals based on the random-effects estimation and subgroup analysis. The advantage of our approach is that the random effects estimation does not rely on the normality assumption, and is more efficient than the random-effect estimator which ignores correlation information from longitudinal data. In addition, the classification tree approach identifies subgroups with similar estimated individual treatment effects. We develop consistency and efficiency theory for the proposed random-effects estimator. Our simulation studies and a data example from AIDS clinical trial also confirm that the proposed method is efficient in identifying an effective treatment strategy for subgroups in finite samples.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.