JSM 2011 Online Program

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Abstract Details

Activity Number: 190
Type: Contributed
Date/Time: Monday, August 1, 2011 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #303429
Title: When does combining markers improve classification performance and what are implications for practice?
Author(s): Aasthaa Bansal*+ and Margaret Sullivan Pepe
Companies: University of Washington and Fred Hutchinson Cancer Research Center
Address: Department of Biostatistics, Seattle, WA, 98195, USA
Keywords: two-phase design ; biomarker combination ; diagnosis ; ROC ; classification

When an existing standard marker does not have sufficient classification accuracy on its own, new markers are sought with the goal of yielding a combination with better performance. The primary criterion for selecting new markers is that they have good performance on their own and preferably be uncorrelated with the standard. We investigate the increment in performance that is possible by combining a novel marker with a moderately performing standard marker under a variety of biologically motivated models for their joint distribution. We find that an uncorrelated continuous marker with moderate performance on its own usually yields only a minimal improvement and identify other settings that lead to large improvements. Our results suggest changing current strategies for identifying markers for possible combination. We find that given studies with large numbers of subjects, a broadened strategy can be more fruitful than the standard strategy that selects panels of markers as candidates based on their marginal performance

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