Robust Approaches to Marker Identification and Evaluation for Risk Assessment

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Robust Approaches to Marker Identification and Evaluation for Risk Assessment

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Title: Robust Approaches to Marker Identification and Evaluation for Risk Assessment
Author: Dai, Wei
Citation: Dai, Wei. 2013. Robust Approaches to Marker Identification and Evaluation for Risk Assessment. Doctoral dissertation, Harvard University.
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Abstract: Assessment of risk has been a key element in efforts to identify factors associated with disease, to assess potential targets of therapy and enhance disease prevention and treatment. Considerable work has been done to develop methods to identify markers, construct risk prediction models and evaluate such models. This dissertation aims to develop robust approaches for these tasks. In Chapter 1, we present a robust, flexible yet powerful approach to identify genetic variants that are associated with disease risk in genome-wide association studies when some subjects are related. In Chapter 2, we focus on identifying important genes predictive of survival outcome when the number of covariates greatly exceeds the number of observations via a nonparametric transformation model. We propose a rank-based estimator that poses minimal assumptions and develop an efficient
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11169769
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