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Risk Prediction Model for Endometrial Cancer

Principal Investigator
Name
Karin Reinhold
Degrees
Ph.D.
Institution
University at Albany, State University of New York
Position Title
Associate Professor
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-382
Initial CDAS Request Approval
Jul 16, 2018
Title
Risk Prediction Model for Endometrial Cancer
Summary
This study aims to build a risk prediction model for endometrial cancer. Previous literature has suggested that risk of endometrial cancer is associated with certain metabolic disorders, age at menopause, body mass index (BMI), parity, history of ovarian cancer diagnosis, type of menopausal hormone therapy (MHT), family history of endometrial 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 endometrial 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 in the Department of Mathematics and Statistics to develop risk prediction models. This study will be part of their graduate educational training in public health to expose students to new methodological and statistical approaches that they may not necessarily obtain in their 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)
Maggie Munkhjargal (University at Albany, State University of New York)
Zhicheng Du (University at Albany, State University of New York)