Threshold quantile regression models with applications to PSA data
Principal Investigator
Name
Eun Ryung Lee
Degrees
Ph.D.
Institution
Sungkyunkwan University
Position Title
Associate Professor
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-893
Initial CDAS Request Approval
Jan 7, 2022
Title
Threshold quantile regression models with applications to PSA data
Summary
In this project, we develop a threshold quantile regression model to analyze Prostate Specific Antigen (PSA) data. Motivated by the empirical studies, the quantile model involves the change point of a PSA level that is relatively measured relative to the detection of prostate cancer, which is possibly unobserved during the whole period of the study. Additionally, we establish suitable statistical estimation and inference method based on the model. Finally, we applied the develop method to an analysis of PSA data.
Aims
The aim of this project can be summarized as follows:
1. Develop a threshold quantile regression model suitable for an analysis of PSA data.
2. Develop a suitable statistical method and theory for estimation and inference in the model.
3. Develop a computational algorithm and provide the computational tasks to the public.
Collaborators
1. Hyokyoung (Grace) Hong, Associate Professor, Department of Statistics and Probability, Michigan State University
2. Mi-Ok Kim, Director, Biostatistics Core, UCSF Helen Diller Family Comprehensive Cancer Center; Professor in Residence, Dept. of Epidemiology & Biostatistics, UCSF
3. Seyoung Park, Assistant Professor, Department of Statistics, Sunkyunkwan University