Abstract Details
Activity Number:
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491
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract - #310283 |
Title:
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Semiparametric Robust Methods for Biomarker Discovery Among Potential Confounders: A Marriage of Targeted Maximum Likelihood Estimation and Limma
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Author(s):
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Sara Kherad-Pajouh*+ and Alan Hubbard and Cliona M. McHale and Luoping Zhang and Martyn T. Smith
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Companies:
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University of California, Berkeley and UC Berkeley and University of California at Berkeley and University of California at Berkeley and University of California at Berkeley
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Keywords:
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High dimensional data ;
Causal inference ;
TMLE ;
Data adaptive methods ;
Variable importance
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Abstract:
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We present semi-parametric variable importance analysis of high dimensional data sets of modest sample size (e.g., gene expression, mRNA, etc), specifically estimating the independent associations among many candidates in the presence of potential confounders. Our methodology is presented in a study of mi-RNA expression and benzene exposure; the consists of not just a large number of comparison, but also trying to tease out of association of the expression of mi-RNA with benzene apart from confounds such as age, race, BMI, etc. Our goal is to propose a method that is reasonably robust in small samples, but does not rely on misspecified (arbitrary) parametric assumptions, and thus will be based on data-adaptive methods. We propose using data-adaptive, loss-based estimation, within the framework of targeted maximum likelihood estimation (TMLE) for estimating variable importance measures (causal inference literature). For inference, we use a general adaptation of the empirical Bayes Limma approach for the targeted parameter, by specification of influence curve for defining an estimate of the sampling variability.
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Authors who are presenting talks have a * after their name.
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