Pseudo-Partial Likelihood Estimators of Cox Regression Model for Biased Sampling Data with Application to Missing Covariates
- 2009-06-19 (Fri.), 14:00 PM
- 中研院-蔡元培館 2F 208 演講廳
- 茶 會:下午13:40統計所蔡元培館二樓
- 蔡 偉 彥 教授
- 美國哥倫比亞大學
Abstract
We obtain a pseudo-partial likelihood for proportional hazards models with biased-sampling data by embedding biased-sampling data into left-truncated data. The log pseudo-partial likelihood of the biased-sampling data is the expectation of the log partial likelihood of the left-truncated data conditioned on the observed data. We also study an application to proportional hazards models with missing covariates. A simulation study demonstrates that, compared with the popular inverse-probability weighted estimators, the new estimators perform better when the observation probability is small, and improve efficiency of estimating the missing covariate effects. Application to a practical example is also reported.