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Activity Number:
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267
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2008 : 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 - #300934 |
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Title:
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Simulation from Structural Survival Models under Complex Time-Varying Data Structures
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Author(s):
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Jessica G. Young*+ and Miguel A. Hernán and Sally Picciotto and James Robins
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Companies:
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Harvard School of Public Health and Harvard School of Public Health and Harvard School of Public Health and Harvard School of Public Health
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Address:
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677 Huntington Avenue , Boston, MA, 02115,
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Keywords:
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causal effects ; marginal structural models ; structural nested models ; g-estimation ; survival analysis ; inverse-probability weighting
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Abstract:
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Standard approaches to estimating the effect of a time-varying exposure on survival may be biased in the presence of time-varying confounders themselves affected by prior exposure. Methods involving estimation of structural models are becoming more widely used alternatives that do not suffer from this bias. In the context of survival outcomes these include inverse probability weighted estimation of the Cox Marginal Structural Model and g-estimation of the Structural Nested Accelerated Failure Time Model. In this paper, we discuss issues related to simulation from these structural survival models.
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