Ovarian Cancer Risk Prediction Model

Principal Investigator

Name
Shao Lin

Degrees
Ph.D.

Institution
University at Albany, State University of New York

Position Title
Professor

Email
slin@albany.edu

About this CDAS Project

Study
PLCO (Learn more about this study)

Project ID
PLCO-374

Initial CDAS Request Approval
Jun 22, 2018

Title
Ovarian Cancer Risk Prediction Model

Summary
This study aims to build a risk prediction model for ovarian cancer. Literature has shown that risk of ovarian cancer is associated with menopausal status, age at menopause, hormone replacement therapy (HRT) use and duration, oral contraceptive (OC) use and duration, parity, number of full-term pregnancies (FTPs), unilateral ovariectomy, body-mass index, and family history of breast and ovarian cancer, as well as numerous other factors. We will use a novel statistical approach (machine learning) to analyze the complex relationship among these risk factors, to determine how they influence ovarian cancer risk. Furthermore, we will compare our models with previous statistical models.

Aims

The overall aim is to have students work under the mentorship of faculty to develop risk prediction models. This study will be part of the graduate educational training in public health to expose students to new methodological and statistical approaches that they may not necessarily obtain in required curriculum.

Collaborators

Miaoling Huang (University at Albany, State University of New York)
Ziqiang Lin (University at Albany, State University of New York)
Jianpeng Xiao (University at Albany, State University of New York)
Wayne Lawrence (University at Albany, State University of New York)
Wang-jian Zhang (University at Albany, State University of New York)
Yi Lu (University at Albany, State University of New York)
Maggie Smith (University at Albany, State University of New York)
Emily Lipton (University at Albany, State University of New York)