Develop a Markovian-based natural history model for ovarian cancer progression

Principal Investigator

Name
Andrew Schaefer

Degrees
Ph.D.

Institution
Rice University

Position Title
Professor, Department of Computational and Applied Mathematics

Email
andrew.schaefer@rice.edu

About this CDAS Project

Study
PLCO (Learn more about this study)

Project ID
PLCO-729

Initial CDAS Request Approval
Jan 28, 2021

Title
Develop a Markovian-based natural history model for ovarian cancer progression

Summary
We aim to develop a Markovian-based model for the progression of ovarian cancer. We specifically investigate the development of our model using biomarkers expressions (e.g., CA 125) alone or in combination with tumor size as the sages of the disease, and plan to calibrate our model using both SEER data and NCI’s dataset to derive the transition probabilities. Our model will provide sex- and histology- specific disease progressions assuming no intervention is undertaken throughout the course of the disease. Accordingly, the model developed under this project can be employed to asses the impact of screening policies and/or cancer interventions on ovarian cancer incidence and mortality rate.

Aims

Develop a Markovian-based natural history model for ovarian cancer by extending the definition of disease stages
Calibrating our model using SEER data and NCI dataset

Collaborators

Mehdi Hemmati, Computational and Applied Mathematics Department, Rice University