Probability and Statistics Seminar
Speaker: Dr. Parichoy Pal Choudhury, KCDH, IIT Bombay
Host: Siuli Mukhopadhyay
Title: Statistical approaches to disease risk prediction with applications in risk-stratified disease prevention
Time, day and date: 11:30:00 AM – 12:30:00 PM, Wednesday, October 07
Venue: Ramanujan Hall
Abstract: Risk-stratified disease prevention involves tailoring of health decisions about screening and prevention based on the individualized risk predictions. This requires a comprehensive understanding of the risk factors, including genetic variants, biomarkers, lifestyle/behavioral and environmental factors leading to the development of a model for predicting absolute risk of a disease of interest. Absolute risk model development requires information on relative risks of the risk factors, population-based age-specific disease incidence rates and competing event rates and population distributions of the risk factors. Such a model needs to be validated ideally in independent prospective cohorts before clinical applications. In this talk, I will describe the Individualized Coherent Absolute Risk Estimator (iCARE) software tool for implementing absolute risk estimation of a disease integrating multiple data sources leveraging the best information available for each of the input parameters and standardized approaches for risk model validation. I will describe multiple recent applications of this tool in the risk prediction of breast, bladder and ovarian cancers. Model validation in two-phase study settings often involve scenarios where expensive biomarkers (e.g., polygenic risk score) are measured in smaller subsample of a prospective cohort and subjects may be selected in the sub-sample using complex sampling designs. I will describe a simple partial risk score-based method for improving precision of model validation statistics (e.g., AUC) using the partial risk factors from the full cohort and complete risk factors from the subsample. I will show two applications of this method: (1) in lung cancer risk prediction with questionnaire-based risk factors and inflammation biomarkers and (2) in breast cancer risk prediction with questionnaire-based risk factors and PRS. If time permits, I will briefly discuss additional ongoing projects.