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Controlled variable selection in Weibull mixture cure models for high-dimensional data.

Han Fu1, Deedra Nicolet2,3, Krzysztof Mrózek2

  • 1Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, Ohio, USA.

Statistics in Medicine
|July 6, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces penalized Weibull mixture cure models (MCMs) for identifying genomic features linked to disease cure and survival. The new method effectively controls false discovery rates, outperforming existing approaches in simulations and AML data analysis.

Keywords:
cure fractionexpectation-maximizationfalse discovery rateforward stagewisesurvival analysis

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Area of Science:

  • Biostatistics
  • Genomics
  • Computational Biology

Background:

  • The mixture cure model (MCM) is crucial for survival analysis when a patient population includes a 'cured' fraction.
  • Identifying genomic features associated with time-to-event outcomes is challenging in high-dimensional data, especially with more predictors than samples.
  • Existing variable selection methods for MCMs are limited in high-dimensional settings.

Purpose of the Study:

  • To develop high-dimensional penalized Weibull mixture cure models (MCMs) for identifying prognostic factors.
  • To enable variable selection for both cure status and survival time in high-dimensional genomic data.
  • To control the false discovery rate (FDR) in variable selection for MCMs.

Main Methods:

  • Developed penalized Weibull MCMs suitable for high-dimensional data.
  • Employed two iterative algorithms for model estimation.
  • Integrated the model-X knockoffs method to control FDR during variable selection.

Main Results:

  • Penalized MCMs demonstrated superior performance across multiple metrics in extensive simulation studies.
  • The proposed method achieved high statistical power while effectively controlling FDR.
  • In an acute myeloid leukemia (AML) application, 14 genes linked to cure and 12 genes linked to time-to-relapse were identified.

Conclusions:

  • The developed high-dimensional penalized Weibull MCMs offer a powerful tool for identifying prognostic genomic factors.
  • The approach effectively manages variable selection in high-dimensional settings with controlled FDR.
  • Findings from the AML application provide potential insights for refining treatment strategies.