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Activity Number: 431
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #319491
Title: Adjusting for Time-Varying Confounding in Survival Analysis Using Structural Nested Cumulative Survivals Models
Author(s): Oliver Dukes* and Stijn Vansteelandt and Shaun Seaman and Torben Martinussen
Companies: Ghent University and Ghent University and University of Cambridge and University of Copenhagen
Keywords: Aalen's additive model ; Causal effect ; G-estimation ; Marginal structural model ; Survival data ; Time-varying confounding

Time-varying confounding forms a pervasive problem in observational studies that attempt to assess the total effect of a time-varying exposure on a survival outcome. Standard regression adjustment for such confounders eliminates indirect effects of early exposures and may induce a 'collider-stratification' bias. Inverse probability weighting for marginal structural survival models overcomes these problems, but can yield unstable inferences and cannot incorporate effect modification by time-varying covariates. We show how these complications can be remedied via novel G-estimation strategies when effects are parameterized on the additive hazard scale. The proposal explicates a close link with inference under structural nested cumulative failure time models, but yields a different class of models, and estimators which accommodate continuous time settings and can be calculated in closed-form. The proposed approach naturally accommodates independent censoring and thus avoids the problem of artificial censoring to which existing G-estimation approaches are susceptible.

Authors who are presenting talks have a * after their name.

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