This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 360
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #309463
Title: Hierarchical Weakest-Link Models in Cytometry of Lung Tumor Samples
Author(s): The Minh Luong*+ and Roger Day
Companies: University of Pittsburgh and University of Pittsburgh
Address: Department of Biostatistics, Graduate School of Public Health, Pittsburgh, PA, 15261,
Keywords: interaction ; joint effects ; epistatis ; regression ; classification ; cytometry

The joint effect of multiple biomarkers strongly associated with outcomes may point to an important molecular mechanism in cancer. The phenotypes of tumors may depend on cancer cells where simultaneous abnormalities occur; cytometry can determine such co-occurrence, while assays on homogenates may discard important information. This leads to the weakest-link paradigm: a biological mechanism's activity depends only on one "weakest-link" covariate, but its identity varies across covariate space. We began with multi-parameter cytometry lung cancer data, obtained from thousands of cells per patient. We used a weakest-link model for the joint effect of four biomarkers within individual cells. This weakest-link model, compared to logic regression and linear regression, performed the best in predicting recurrence-free survival, according to cross-validation criteria.

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