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Activity Number: 402 - Statistical Methods for New Challenges in Lifetime/Complex Data
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Lifetime Data Science Section
Abstract #309762
Title: Score Tests with Incomplete Covariates and High-Dimensional Auxiliary Variables
Author(s): Kin Yau Alex Wong*
Companies: The Polytechnic University of Hong Kong
Keywords: Association tests; Imputation; Lasso; Post-selection inference

Analysis of modern biomedical data is often complicated by the presence of missing values. To improve statistical efficiency, it is desirable to make use of potentially high-dimensional observed variables to impute or predict the missing values. Although many methods have been developed for prediction using high-dimensional variables, it is challenging to perform valid inference based on the predicted values. In this presentation, we develop an association test for an outcome variable and a potentially missing covariate, where the covariate can be predicted using a set of high-dimensional auxiliary variables. We use LASSO to estimate the model for the incomplete covariate and adopt a conditional likelihood approach to accommodate the estimation variability. The method is applicable to general outcome variables, including censored time-to-event variables. We demonstrate the validity of the proposed method and its advantages over existing methods through extensive simulation studies and provide an application to a major cancer genomics study.

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

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