Rank Tests for Time-to-Event Data Following Propensity Score Matching with Replacement
However, the inference on time-to-event data by the PSM with replacement remains unsolved. There are two challenges. 1) the PSM with replacement is a non-smooth process; 2) how to deal with the censoring indicator in the time-to-event data. In this project, we aim to develop a class of statistical tests for the time-to-event data by the PSM with replacement.
1) develop a class of statistical tests for the time-to-event data by the PSM with replacement
2) Analyze the NLST data as a real example for the new proposed statistical methods. We try to compare the the PFS/OS of patients treated by different therapy in the NLST data.
Shanmei Liao, Beigene;
Yujie Zhong, School of Statistics and Management, Shanghai University of Finance and Economics