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Activity Number: 304
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract - #304670
Title: Causal Estimations for the Semiparametric Transformation Models with Prevalent Sampling
Author(s): Yu-Jen Cheng*+
Companies: National Tsing Hua University
Address: No. 101, Section 2, Kuang-Fu Road,, HsinChu 300, , Taiwan, Republic of China
Keywords: Causal estimation ; Prevalent sampling ; Propensity scores ; Survival analysis
Abstract:

In this paper, we consider how to estimate both causal survival functions and the truncation distribution from semiparametric transformation models only using prevalent survival data. Two statistical issues are mainly focused: missingness and dependency. The missingness in data comes from two different sources: One is due to the hypothetical potential outcome framework; the other is caused by the prevalent sampling scheme since the outcome measurements as well as the covariates are partially observed. For dependency, even after condition on covariates, the truncation and failure times are allowing to have an unspecific dependency structure. Moreover, due to such missingness and dependency, we show that the propensity scores need to be adjusted for performing the correct sensitivity analysis. Statistical analysis without considering such missingness and dependency will lead to biased results in causal inference. Large sample properties of our estimators based on empirical processes are derived in this article. The proposed method was motivated by and applied to the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data for women diagnosed with breast cancer.


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