Online Program Home
My Program

Abstract Details

Activity Number: 649
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #321009 View Presentation
Title: Classification of Mixed Binary Outcomes Using Biomarker Information
Author(s): Feng-Chang Lin* and Quefeng Li
Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
Keywords: Amplicon deep sequencing ; Hypnozoite ; Malaria relapse ; Model-based method ; Plasmodium Vivax

Classification of mixed binary outcomes is commonly seen in infectious disease when the disease outcome could consist of either relapse from previous occurrence of the disease or completely random re-infection. However, while a conventional approach using the baseline information may give a naïve classification of the outcome, little literature has suggested how to use the biomarker information attached to the event for classification, especially when the biomarker information is time-varying. In this talk, we will first review the data structure using Plasmodium Vivax infections in malaria as an example, and then proposes a model-based method using biomarker information to enhance the accuracy of classification. Our approach will be tested in extensive simulation experiments when there is a true underlying outcome, with superior sensitivity and specificity for better performance of prediction. The malaria infection data will be revisited using our proposed method.

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

Back to the full JSM 2016 program

Copyright © American Statistical Association