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Activity Number: 11 - Recent Advances in Statistical Methods for Large-Scale Complex Biomedical Data
Type: Invited
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #300584
Title: Uncertainty Quantification of Treatment Regime with High-Dimensional Covariates
Author(s): Sijian Wang* and Minge Xie and Yilei Zhan
Companies: Rutgers University and Rutgers University and Rutgers University
Keywords: Adaptive Trial; Regularization; Precision Medicine; Variable Selection; Confidence distribution

Personalized decision rule in precision medicine is a `discrete parameter', for which theoretical development of statistical inference is lacking. With the advance of recent technology and data management, the personalized decision rules are usually constructed based on a large number of patients’ characteristics. This high-dimensionality makes the inference on the decision rule even more challenging. Based on the potential outcome framework and confidence distribution (CD) framework, we propose a confidence measure to quantify the estimation uncertainty in a personalized decision with high-dimensional covariates. This measure, with value in [0,1], provides a frequency-based assessment about the decision. It is also shown to match well with the classical assessments of sensitivity and specificity, but without the need to know the true optimal treatment regime. Utility of the development is demonstrated in an adaptive clinical trial.

Authors who are presenting talks have a * after their name.

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