Online Program Home
My Program

Abstract Details

Activity Number: 278 - Combining Markers for Classification in Practical Tasks
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Medical Devices and Diagnostics
Abstract #326478 Presentation
Title: New Dimension Reduction Methods for Combining Longitudinally Measured Biomarkers
Author(s): Ruth Pfeiffer * and Wei Wang and Efstathia Bura
Companies: National Cancer Institute and George Washington University and Vienna University of Technology
Keywords: Inverse regression; Kronecker product; Non-linear dimension reduction

Sufficient dimension reduction (SDR) aims to find a low dimensional transformation of the predictors that preserves all of most of their information about the outcome. In earlier work we developed nonparametric SDR methods to combine several diagnostic markers that are measured longitudinally, using information on correlations over time and across markers (Pfeiffer et al., 2012). Here, we extend parametric SDR approaches, using least squares methods (parametric inverse regression, PIR, Bura and Cook, 2001) and maximum likelihood estimation (Principal Fitted Components, PFC, Cook and Forzani, 2008) to accommodate the longitudinal structure of multiple biomarkers measured over time and improve efficiency of the estimation of the reduction. Robustness to parametric assumptions and hte performance are studied in simulations, and we illustrate the methods with a real data example. This is joint work with Wei Wang and Efstathia Bura

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

Back to the full JSM 2018 program