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Related Experiment Video

Updated: May 20, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Non-parametric Evaluation of Biomarker Accuracy under Nested Case-control Studies.

Tianxi Cai1, Yingye Zheng

  • 1Department of Biostatistics, Harvard University, Boston, MA, USA.

Journal of the American Statistical Association
|July 31, 2012
PubMed
Summary

This study introduces new methods to accurately measure risk markers in nested case-control studies. These non-parametric estimators improve predictive accuracy assessment for cost-effective prospective research.

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Last Updated: May 20, 2026

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

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Published on: October 11, 2018

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Evaluating new risk markers requires accurate predictive assessment in prospective studies.
  • Obtaining marker data for all participants is often infeasible, necessitating efficient designs like the nested case-control (NCC) design.
  • Existing analysis methods for NCC studies primarily focus on association measures, leaving a gap in accuracy assessment.

Purpose of the Study:

  • To propose non-parametric estimators for assessing predictive accuracy in nested case-control studies.
  • To address the analytical challenges posed by the complex, outcome-dependent sampling in NCC designs.
  • To provide robust statistical tools for evaluating the clinical utility of novel risk markers.

Main Methods:

  • Development of non-parametric estimators for commonly used accuracy measures.
  • Derivation of asymptotic expansions for accuracy estimators under both finite population and Bernoulli sampling.
  • Establishment of asymptotic equivalence between finite population and Bernoulli sampling estimators.

Main Results:

  • Proposed non-parametric procedures demonstrate good performance in finite samples through simulations.
  • The new methods provide a statistically sound approach to estimating predictive accuracy in NCC studies.
  • Asymptotic equivalence was established between different sampling frameworks, simplifying analysis.

Conclusions:

  • The developed non-parametric estimators offer a valuable tool for analyzing predictive accuracy in nested case-control studies.
  • These methods enhance the ability to evaluate new risk markers cost-effectively.
  • The study provides a practical framework, illustrated with Framingham Offspring study data, for improved epidemiological research.