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Biomarker evaluation under imperfect nested case-control design.

Xuan Wang1, Yingye Zheng2, Majken Karoline Jensen3

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

Statistics in Medicine
|April 29, 2021
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Summary
This summary is machine-generated.

This study introduces a new method to estimate sampling probabilities in nested case-control (NCC) studies for biomarker research. The improved approach enhances prediction model evaluation for cardiovascular risk using clinical biomarkers.

Keywords:
finite population samplinginverse probability weightingnonparametric smoothingresamplingrisk prediction

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Biomarker Research

Background:

  • The nested case-control (NCC) design is a cost-effective method for biomarker research, sampling cases and controls from risk sets.
  • Existing methods for evaluating risk model prediction performance in NCC studies rely on inverse probability weighting.
  • Current probability estimation strategies often fail due to model mis-specification or the curse of dimensionality.

Purpose of the Study:

  • To propose a novel strategy for estimating sampling probabilities in NCC studies.
  • To develop a robust method for variance estimation in the context of complex correlation structures from risk set sampling.
  • To improve the evaluation of prediction performance for risk models using clinical biomarkers.

Main Methods:

  • A varying coefficient model is proposed to estimate sampling probabilities, balancing robustness and the curse of dimensionality.
  • A perturbation resampling procedure is introduced to address the failure of standard resampling for variance estimation.
  • The method was applied to the Nurses' Health Study II to develop and evaluate cardiovascular risk prediction models.

Main Results:

  • Simulation studies demonstrate that the proposed method performs well in finite samples.
  • The varying coefficient model provides a more robust estimation of sampling probabilities compared to existing methods.
  • The perturbation resampling procedure yields valid interval estimation for the proposed estimators.

Conclusions:

  • The proposed varying coefficient model and perturbation resampling offer a robust approach for analyzing data from nested case-control studies.
  • This method enhances the evaluation of prediction models, particularly in biomarker research for diseases like cardiovascular disease.
  • The application to the Nurses' Health Study II validates the utility of the proposed method in real-world epidemiological research.