【專題演講】Robust Best Linear Weighted Estimator with Missing Covariates in Survival Analysis(3/21)
- 日期 : 2024/03/21 14:00
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主講人 : Professor Ching-Yun Wang（王清雲教授）
- 地點 : 人社中心第一會議室及webex線上會議室（hybrid）
- Webex會議資訊 : 會議室連結：https://reurl.cc/G4LgMG
會議號：2519 939 0809
密碼：0321
- 演講摘要 : Missing data in the covariates can result in biased estimates and loss of power to detect associations. We consider Cox regression in which some covariates are subject to missing. The inverse probability weighted approach is often applied to regression analysis with missing covariates. Inverse probability weighted estimators typically are less efficient than likelihood-based estimators, but in general are more robust against model misspecification. In this research, we propose a robust best linear weighted estimator for Cox regression with missing covariates. Our proposed estimator is the projection of the simple inverse probability weighted estimator onto the orthogonal complement of the score space based on a working regression model of the observed data. The efficiency gain is from the use of the association between the survival outcome variable and the available covariates, which is the working regression model. The asymptotic distribution is derived, and the finite sample performance of the proposed estimator is examined via extensive simulation studies. The methods are applied to a colorectal cancer study to assess the association of the microsatellite instability status with colorectal cancer–specific mortality.
- 主辦單位 : 調查研究專題中心
- 聯絡人 : csrevent@gate.sinica.edu.tw 謝小姐
