Abstract Details
Activity Number:
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526
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract - #308574 |
Title:
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Efficient Estimation of the Attributable Fraction When There Are Monotonicity Constraints and Interactions
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Author(s):
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Wei Wang*+ and Dylan S Small
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Companies:
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and University of Pennsylvania
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Keywords:
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Attributable fraction estimation ;
logistic regression ;
Monotone regression with interactions
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Abstract:
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The PAF for an exposure is the fraction of disease cases in a population that can be attributed to that exposure. One method of estimating the PAF involves estimating the probability of having the disease given the exposure and confounding variables. In many settings, the exposure will interact with the confounders and the confounders will interact with each other. Also, in many settings, the probability of having the disease is thought, based on subject matter knowledge, to be a monotone increasing function of the exposure and possibly of some of the confounders. We develop an efficient approach for estimating logistic regression models with interactions and monotonicity constraints, and apply this approach to estimating the population attributable fraction (PAF). Our approach produces substantially more accurate estimates of the PAF in some settings than the usual approach which uses logistic regression without monotonicity constraints.
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Authors who are presenting talks have a * after their name.
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