Estimating the Capacity for Improvement in Risk Prediction with a Marker

• Activity Number: 179
• Type: Topic Contributed
• Date/Time: Monday, August 4, 2008 : 2:00 PM to 3:50 PM
• Sponsor: Section on Health Policy Statistics
• Author(s): Wen Gu*+
• Companies: University of Washington
• Keywords: Classification ; diagnostic likelihood ratio ; posterior probability ; logistic regression ; diagnostic test ; biomarker

Abstract

Consider a set of baseline predictors X to predict a binary outcome D and let Y be a novel marker or predictor. This paper is concerned with evaluating the performance of the augmented model P(D=1|Y,X) compared with the baseline model P(D=1|X). The diagnostic likelihood ratio, DLR(Y), quantifies the change in risk obtained with knowledge of Y=y for a subject with baseline risk factors X. The notion has been promoted as a way to capture the increment in risk prediction due to Y. We propose methods for making inference about DLRX(Y). We also demonstrate how the population performance of baseline and augmented risk models can be compared using baseline data from a cohort and marker data from a nested case-control study. Finally, we show how the methodology yields estimates of covariate specific predictiveness curves that can be used by an individual to decide the ascertainment of Y.