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Activity Number: 595
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #308607
Title: Estimation of a Direct Effect When Considering a Time-to-Event Response
Author(s): Torben Martinussen*+
Companies:
Keywords: Cox-model ; causal effect ; direct effect
Abstract:

We are interested in estimating the direct effect of an exposure variable X on a survival outcome T. In case of an intermediate variable K and an unobserved confounder U for the effect of K on T standard regression techniques will render a biased estimate ofthe direct effect of X on T. This problem may be solved with the inclusion of additional information, L, that removes the effect of U on K. However, if L is also affected by X then standard methods are still not appropriate. Marginal structural models have been suggested to tackle this problem but they need estimation of specific weights that may be quite unstable. To overcome this problem, Goetgeluk et al. (JRSSB, 2009) suggested a so-called G-estimation approach in the case of an un-censored response variable. In this talk I show how to generalize their approach to the setting of survival data. I will describe the methodology in detail for the Aalen additive hazards model and also give some calculations for the Cox model where it is more difficult to derive estimators.


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