American Statistical Association
New York City
Metropolitan Area Chapter

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



Ruoqing Zhu
Assistant Professor
Department of Statistics
University of Illinois at Urbana

Host: Dr. Yifei Sun


Learning an individualized dose rule (IDR) in personalized medicine is a challenging statistical problem. Existing methods for estimating the optimal IDR often suffer from the curse of dimensionality, especially when the IDR is learned nonparametrically using machine learning approaches. To tackle this problem, we propose a dimension reduction framework. The proposed framework exploits that the IDR can be reduced to a nonparametric function which relies only on a few linear combinations of the original covariates, hence leading to a more parsimonious model. To achieve this, we propose two approaches, a direct learning approach that yields the IDR as commonly desired in personalized medicine, and a pseudo-direct learning approach that focuses more on learning the dimension reduction space. Under regularity assumptions, we provide the convergence rate for the semiparametric estimators and Fisher consistency properties for the corresponding value function. For the pseudo-direct learning estimator, we use an orthogonality constrained optimization approach on Stiefel manifold to update the dimension reduction space. For the direct learning approach, we use an alternative updating scheme that iteratively updates the dimension reduction space and the nonparametric optimal dose rule function. The performances of the proposed methods are evaluated through simulation studies and a warfarin pharmacogenetic dataset.

Date: Thursday, February 28, 2019
Time: 11:45 A.M. - 12:45 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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