Precision Medicine

Organizer: Lingsong Zhang, Associate Professor of Statistics, Department of Statistics, Purdue University

Chair: Arman Sabbaghi, Assistant Professor of Statistics, Department of Statistics, Purdue University

Speakers

Guanhua Chen, Assistant Professor, Department of Biostatistics and Medical Informatics, University of Wisconsin
Bruce Craig, Professor of Statistics and Director of Statistical Consulting, Department of Statistics, Purdue University
Haoda Fu, Research Advisor, Biometrics and Advanced Analytics, Eli Lilly and Company

Schedule

Friday, June 8, 10:00 a.m.-12:00 p.m. in STEW 214 CD

Time	Speaker	Title
10:00-10:40 a.m.	Guanhua Chen	Constructing Stabilized Dynamic Treatment Regimes
	Abstract: We propose a new method termed stabilized O-learning for deriving stabilized dynamic treatment regimes (DTRs), which are sequential decision rules for individual patients not only adapt over the course of the disease progression but also consistent over time in its format. The method provides a robust and efficient learning framework for constructing DTRs by directly optimizing a doubly robust estimator of the expected long-term outcome. It can accommodate various types of outcomes, including continuous, categorical and potentially censored survival outcomes. In addition, the method is flexible to incorporate clinical preferences into a qualitatively fixed rule, where the parameters indexing the decision rules that are shared across stages can be estimated simultaneously. We conducted extensive simulation studies, showing a superior performance of the proposed method. We analyzed the data from the prospective Canary Prostate Cancer Active Surveillance study using the proposed method.
10:40-11:20 a.m.	Bruce Craig	Distance Weighted Discrimination Approach for Precision Medicine
	Author: Hui Sun, Bruce Craig and Lingsong Zhang Abstract: Outcome Weighted Learning Approach has been popular in precision medicine applications. For observational studies, the current approaches suffer from imbalance and high dimensionality issues. Extending the current support vector machine-based approached to distance weighted discrimination (DWD) approach will overcome these issues. In this paper, we prove that DWD based outcome weighted learning approach is Fisher consistent and not sensitive to imbalance issue. Empirical studies also shows that DWD based approach outperform SVM-based methods.
11:20 a.m.-12:00 p.m.	Haoda Fu	Individualized Treatment Recommendation (ITR) for Survival Outcomesn
	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 enjoy the following practical and theoretical features · Instead of fitting the data, our method directly search the optimal treatment police 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).