Friday, February 28, 2020 3pm to 4pm
About this Event
25 Park Place, Atlanta, GA
Title: Novel Marginal Screening Methods for High-dimensional Survival Data
Speaker: Dr. Tzu-Jung Huang, Data Scientist at Videa, COX Enterprise, Atlanta
Abstract: This talk introduces two novel methods of screening high-dimensional predictors for survival outcomes. Motivated by high-throughput genomic data for diffuse large-B-cell lymphoma, the first approach introduces a marginal screening test to detect the presence of significant predictors for a right censored survival outcome, named Adaptive Resampling Test for Survival data (ARTS). This approach is designed under a high-dimensional accelerated failure time (AFT) model; adopts the test statistic based on the maximally selected estimator from a marginal AFT working model, and applies a regularized bootstrap method to calibrate the test. This testing procedure is more powerful and less conservative than both a Bonferroni correction of the marginal tests and other competing methods. The second screening approach is based on a more efficient estimator than the one used in ARTS. This proposal circumvents the computationally expensive bootstrap resampling required for ARTS, which enables it to address screening problems with ultrahigh-dimensional predictors. This is a joint work with Alex Luedtke (Department of Statistics, University of Washington) and Ian W. McKeague (Department of Biostatistics, Columbia University).
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