This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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84
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Type:
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Contributed
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Date/Time:
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Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract - #308342 |
Title:
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Multiplicative Semiparametric Regression Model for Relative Risk with Application to Air Pollution Exposure
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Author(s):
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Catherine Tuglus*+ and Kristin E. Porter and Mark J. Van der Laan
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Companies:
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University of California, Berkeley and University of California, Berkeley and University of California, Berkeley
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Address:
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, , , USA
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Keywords:
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Relative Risk ;
Semiparametric Regression ;
Targeted Maximum Likelihood ;
Double Robustness ;
Air Pollution ;
Causal Inference
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Abstract:
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The relative risk (RR) is a popular measure of the exposure or treatment effect on a binary outcome. When the outcome is common, estimation of the RR becomes problematic if the exposure or any of the covariates are continuous. We propose a new estimation procedure that targets the adjusted relative risk for common outcomes under a log-linear semiparametric model. Based on targeted Maximum Likelihood theory, the method provides double robust and locally efficient estimates of the adjusted RR with correct inference and causal interpretation under appropriate assumptions. The robustness of our estimator under model misspecification is compared to alternative methods (e.g. log-linear, Poisson regression) through simulation. We apply our method to a study of air pollution effects on respiratory health in children with asthma.
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