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Kernel machine testing for risk prediction with stratified case cohort studies.

Rebecca Payne1, Matey Neykov1, Majken Karoline Jensen1

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A.

Biometrics
|December 23, 2015
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Summary
This summary is machine-generated.

This study introduces new statistical methods for analyzing rare disease outcomes using case-cohort (CCH) designs. The proposed approach improves the identification of complex genetic markers for disease risk prediction.

Keywords:
Case cohortCox proportional hazards modelFinite-population samplingInverse probability weightingKernel machine regressionRisk predictionVariance component test

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Genetics

Background:

  • Large cohorts with biospecimens are crucial for identifying novel risk prediction markers.
  • Case-cohort (CCH) designs conserve resources for rare outcomes but present analytical challenges.
  • Existing methods struggle with complex, non-linear marker effects in CCH data.

Purpose of the Study:

  • To develop advanced statistical methods for analyzing complex marker effects in case-cohort studies.
  • To enable robust identification of marker sets associated with disease risk.
  • To address the analytical complexities of outcome-dependent, finite-population sampling in CCH designs.

Main Methods:

  • Proposed inverse probability weighted (IPW) variance component tests within a Cox proportional hazards kernel machine (CoxKM) regression framework.
  • Developed adaptive procedures to combine information from multiple kernels for optimal power.
  • Introduced a novel perturbation resampling scheme to accurately approximate complex null distributions and account for CCH sampling correlations.

Main Results:

  • The proposed IPW CoxKM testing procedures demonstrate good performance in finite samples through extensive simulations.
  • The methods effectively handle complex non-linear marker effects, a limitation of previous approaches.
  • The novel resampling scheme accurately captures the correlation structure induced by CCH sampling.

Conclusions:

  • The developed IPW CoxKM methods provide a powerful and flexible tool for analyzing case-cohort data, especially for complex marker effects.
  • These methods enhance the ability to identify novel risk prediction markers from large biospecimen collections.
  • The approach was successfully applied to a study of Apolipoprotein C-III markers and coronary heart disease risk.