STATISTICS COLLOQUIUM
Haoda Fu, Ph.D.
Research Advisor,
Biometrics and Advanced Analytics
Eli Lilly and Company
Individualized Treatment Recommendation (ITR) for Survival Outcomes
Abstract
ITR is a method to recommend treatment based on individual patient characteristics to maximize clinical benefit. During the past a few years, we have developed and published methods on this topic with various applications including comprehensive search algorithms, tree methods, benefit risk algorithm, multiple treatment & multiple ordinal treatment algorithms. In this talk, we propose a new ITR method to handle survival outcomes for multiple treatments. This new model enjoys the following practical and theoretical features
• Instead of fitting the data, our method directly search the optimal treatment policy which improve the efficiency
- To adjust censoring, we propose a doubly robust estimator. Our method only requires either censoring model or survival model is correct, but not both. When both are correct, our method enjoys better efficiency
- Our method handles multiple treatments with intuitive geometry explanations
- Our method is Fisher’s consistent even under either censoring model or survival model misspecification (but not both).
DATE: Wednesday, October 4, 2017
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 105
Coffee will be served at 3:30 am in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu