DeepAI AI Chat
Log In Sign Up

A Modified Net Reclassification Improvement Statistic

by   Glenn Heller, et al.
Memorial Sloan Kettering Cancer Center

The continuous net reclassification improvement (NRI) statistic is a popular model change measure that was developed to assess the incremental value of new factors in a risk prediction model. Two prominent statistical issues identified in the literature call the utility of this measure into question: (1) it is not a proper scoring function and (2) it has a high false positive rate when testing whether new factors contribute to the risk model. For binary response regression models, these subjects are interrogated and a modification of the continuous NRI, guided by the likelihood-based score residual, is proposed to address these issues. Within a nested model framework, the modified NRI may be viewed as a distance measure between two risk models. An application of the modified NRI is illustrated using prostate cancer data.


page 1

page 2

page 3

page 4


Investigating Critical Risk Factors in Liver Cancer Prediction

We exploit liver cancer prediction model using machine learning algorith...

Nonparametric regression with modified ReLU networks

We consider regression estimation with modified ReLU neural networks in ...

A modified maximum contrast method for unequal sample sizes in pharmacogenomic studies

In pharmacogenomic studies, biomedical researchers commonly analyze the ...

Modified ResNet Model for MSI and MSS Classification of Gastrointestinal Cancer

In this work, a modified ResNet model is proposed for the classification...

A functional-model-adjusted spatial scan statistic

This paper introduces a new spatial scan statistic designed to adjust cl...

Improving likelihood-based inference in control rate regression

Control rate regression is a diffuse approach to account for heterogenei...