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Activity Number: 139
Type: Contributed
Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #316865 View Presentation
Title: Recovering Marginal Treatment Effects from a Transition Model for Longitudinal Data with Drop Out Using Path Analysis
Author(s): Emin Tahirovic*
Companies: University of Pennsylvania
Keywords: direct/indirect effects ; SWEEP estimator ; path analysis ; Doob's decomposition

We show how to recover marginal treatment estimates from a transitional incremental model with martingale random effects working assumption by using the generalized SWEEP (G-SWEEP) estimator. Conditioning on intermediate outcomes is often used in settings with generally non-ignorable dropout to facilitate unbiased estimation in such cases. Estimates recovered from such a strategy are conditional on the past outcomes. Combining this with path analysis method that can recover marginal treatment effects from the conditional ones yields a method that effectively adjusts for non-ignorable drop-out and can be used to unbiasedly estimate total effect of the treatment. The application of the G-SWEEP estimator to incremental longitudinal data performs an implicit path analysis and recovers the effect of treatment at baseline which is generally masked by conditioning on intermediate outcomes.

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

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