Rank Tests for Time-to-Event Data Following Propensity Score Matching with Replacement
Principal Investigator
Name
Zhiyue Huang
Degrees
Ph.D.
Institution
Beigene
Position Title
Statistician
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-657
Initial CDAS Request Approval
Apr 16, 2020
Title
Rank Tests for Time-to-Event Data Following Propensity Score Matching with Replacement
Summary
With the increasing accessibility of electric health data and raising costs and recognized limitation of traditional trials, how to use real-world data and observational studies to enhance the efficacy evidence and bridge the gap between trials and practice attracts interests again. To handle confounding, randomization remains as a key tool, while customized statistical methodologies may be applied in real-world data and observational studies. The propensity score matching (PSM) is one of the popular methods to handle confounding in observational studies.
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.
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.
Aims
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.
Collaborators
Shanmei Liao, Beigene;
Yujie Zhong, School of Statistics and Management, Shanghai University of Finance and Economics