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Activity Number: 582 - Integrating Information from Different Data Sources: Some New Developments
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
Sponsor: International Chinese Statistical Association
Abstract #309761
Title: Integrating Information from Existing Risk Prediction Models with No Model Details
Author(s): Peisong Han* and Jeremy Taylor and Bhramar Mukherjee
Companies: University of Michigan and University of Michigan and University of Michigan
Keywords: Auxiliary information; Data integration; Empirical likelihood; Estimating equations; Estimation efficiency

Consider the setting where (i) individual-level data are collected to build a regression model for the association between observing an event of interest and certain covariates, and (ii) some risk calculators predicting the risk of the event using less detailed covariates are available, possibly as black boxes with little information available about how they were built. We propose a general empirical-likelihood-based framework to integrate the rich auxiliary information contained in the calculators into fitting the regression model in order to improve the efficiency for the estimation of regression parameters. As an application, we study the dependence of the risk of high grade prostate cancer on both conventional risk factors and newly identified biomarkers by integrating information from the Prostate Biopsy Collaborative Group (PBCG) risk calculator, which was built based on conventional risk factors alone.

Authors who are presenting talks have a * after their name.

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