Semiparametric and Nonparametric Models for Estimation of Causal Effects by Principal Stratification with Outcomes Truncated by Death

• Activity Number: 311
• Type: Invited
• Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Social Statistics Section
• Author(s): Xiao-Hua (Andrew) Zhou *+
• Companies: University of Washington
• Keywords: Causal inference ; Truncation by Death ; Principal Stratification

Abstract

There are many situations where patient's outcomes are truncated by death, in which patients die before outcomes of interest are measured. In this talk we consider identifiability and estimation of causal effects by principal stratification when some outcomes are truncated by death. In this talk, we introduce a new method for identifying the causal parameter of interest under a non-parametric and semi-parametric model. We also propose new estimation methods for the causal parameters of interest. We conduct several simulation studies to evaluate the finite-sample performance of the proposed approach. Finally, we apply the proposed approach to a real data set from a Southwest Oncology Group (SWOG) clinical trial. This is a joint work with Drs. Ding, Geng, and Yan.