140.742.01
Risk Prediction and Precision Medicine
Location
East Baltimore
Term
2nd Term
Department
Biostatistics
Credit(s)
3
Academic Year
2022 - 2023
Instruction Method
In-person
Tu, Th, 10:30 - 11:50am
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Other Year
Resources
Prerequisite
Biostatistics 140.651 or equivalent, and 140.641 (Survival Analysis I ). Knowledge of probability and statistical theory is required. Non-Biostatistics students need permission from instructor.
Covers two parts: Part I (taught by Dr. Wang) and Part II (taught by Dr. Chatterjee). Part I: Covers various topics for analyzing prospective and case-control data, and for evaluating the performance of biomarkers to predict risk of clinical or disease outcome. Specific topics include concepts and methods for relative risk, absolute risk, odds/hazards ratio, and ROC/AUC biomarker inference for binary and failure time outcomes. Part II: Covers methods for building absolute risk models and incorporating polygenic risk score (PRS) into such models together with non-genetic factors. Specific topics include a) Absolute risk and components of underlying models b) Integration of Data from various sources for building absolute risk models c) Concept of heritability and its relationship with predictive performance of PRS d) Methods for building PRS from existing genome-wide association studies e) Evaluating statistical validity and clinical utility of risk-prediction models.
Learning Objectives
Upon successfully completing this course, students will be able to:
- Conduct statistical analysis for evaluating prediction performance of biomarkers and diagnostic tests
- Have a better ability to read and understand papers published in statistical and medical journals on related topics
- Have better preparation to work on dissertation topics or advanced biostatistics
Methods of Assessment
This course is evaluated as follows:
- 50% Take-home project
- 50% In-class presentation