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Activity Number: 461 - Design and Analytic Approaches to Address Unmeasured Confounding
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #309829
Title: Nonparametric Instrumental Variable Estimators for Survival Outcomes
Author(s): Youjin Lee* and Edward Kennedy and Nandita Mitra
Companies: University of Pennsylvania and Carnegie Mellon University and University of Pennsylvania
Keywords: Instrumental variable; Nonparametric estimation; Censoring; Local average treatment effect

Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal inference. However, most IV methods are only applicable to discrete or continuous outcomes and there are few IV methods for censored survival outcomes. In this work we propose nonparametric estimators for the local average treatment effect on survival probabilities under both nonignorable and ignorable censoring. We provide an efficient influence function-based estimator and a simple estimation procedure when the IV is either binary or continuous. The proposed estimators possess double-robustness properties and can easily incorporate nonparametric estimation using machine learning tools. In simulation studies, we demonstrate how flexible and efficient our proposed estimators are under various scenarios. We apply our method to the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial for estimating the causal effect of screening on survival probabilities and investigate the causal contrasts between the two interventions under different censoring assumptions.

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

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