Abstract Details
Activity Number:
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416
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract - #308635 |
Title:
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Exploring the Finite-Sample Properties of Inverse Probability Weighted and G Estimation of a Structural Nested Failure Time Model Under Positivity Violations
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Author(s):
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Ashley Isaac Naimi*+ and Stephen R. Cole and Erica E. M. Moodie and Jay Kaufman
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Companies:
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McGill University and The University of North Carolina at Chapel Hill and McGill University and McGill University
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Keywords:
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G-estimation ;
Structural Nested Model ;
Causal Inference ;
Positivity ;
Finite-Sample Properties
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Abstract:
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When seeking to estimate the effect of a time-dependent exposure on a failure time outcome, time-dependent confounding may be a concern. The inverse probability weighted (IPW) estimator is consistent for the parameter of a structural failure time model under conditions of time-dependent confounding when positivity holds. This assumption requires that, conditional on relevant confounders, the probability of being exposed is bounded away from zero and one. The semiparametric G-estimator is consistent for the parameter of a structural nested failure time model, even under nonpositivity. In this talk, we explore the finite sample properties of the G-estimation of a structural nested failure time model and compare them to IPW estimators of an equivalent model under different degrees of nonpositivity.
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Authors who are presenting talks have a * after their name.
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