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Variable selection for case-cohort studies with failure time outcome.

A I Ni1, Jianwen Cai1, Donglin Zeng1

  • 13101 McGavran-Greenberg Hall, CB 7420, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.

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Summary

This study introduces a variable selection method for case-cohort studies, enhancing efficiency in large datasets. The proposed procedure demonstrates consistent variable selection and an oracle property, outperforming traditional criteria.

Keywords:
Case-cohort designDiverging number of parametersOracle propertySmoothly clipped absolute deviationSurvival analysisVariable selection

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Genetics

Background:

  • Case-cohort designs reduce covariate measurement costs in large cohort studies.
  • Large studies often involve numerous covariates, necessitating efficient variable selection.

Purpose of the Study:

  • To evaluate a variable selection method using the smoothly clipped absolute deviation (SCAD) penalty in case-cohort designs.
  • To assess the properties of the maximum penalized pseudo-partial-likelihood estimator with a diverging number of parameters.

Main Methods:

  • Utilized the smoothly clipped absolute deviation (SCAD) penalty for variable selection.
  • Established consistency and asymptotic normality of the penalized pseudo-partial-likelihood estimator.
  • Conducted simulation studies to compare with Akaike information criterion (AIC) and Bayesian information criterion (BIC) based methods.

Main Results:

  • The proposed SCAD-based method demonstrated consistent variable selection.
  • The method exhibited an asymptotic oracle property.
  • Simulation results indicated favorable finite-sample performance.

Conclusions:

  • The SCAD penalty offers an efficient and consistent variable selection approach for case-cohort studies.
  • The method is recommended for large-scale epidemiological research, as demonstrated in the Busselton Health Study.