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Updated: May 18, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

A bias-corrected net reclassification improvement for clinical subgroups.

Nina P Paynter¹, Nancy R Cook¹

¹Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA (NPP and NRC)

Medical Decision Making : an International Journal of the Society for Medical Decision Making

|October 9, 2012

Summary

A new bias-corrected clinical Net Reclassification Improvement (NRI) method provides unbiased estimates for prediction models within subgroups. This approach reduces overly optimistic results in clinical risk prediction research.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Area of Science:

Biostatistics
Epidemiology
Clinical Prediction Models

Background:

Assessing prediction model performance in specific patient subgroups is clinically important.
Current methods for evaluating reclassification within subgroups can yield biased results.

Purpose of the Study:

To develop and demonstrate an unbiased method for estimating Net Reclassification Improvement (NRI) within a subset of patients (clinical NRI).
To provide a bias-corrected measure for evaluating prediction model reclassification in intermediate-risk groups.

Main Methods:

Derived the expected value of the clinical NRI under the null hypothesis.
Conducted a simulation study using a logistic model with known and potential predictors.
Validated the bias-corrected clinical NRI using data from the Women's Health Study.

Main Results:

The bias-corrected clinical NRI estimate demonstrated a mean of zero under null conditions across simulations.
The proposed method proved to be unbiased, unlike naive estimates.
Two methods for variance estimation showed acceptable type 1 error rates.

Conclusions:

The proposed bias-corrected clinical NRI method offers an improvement over existing techniques.
This method is recommended to mitigate overly optimistic performance estimates in clinical prediction models.