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A multiple testing procedure to associate gene expression levels with survival.

Sin-Ho Jung1, Kouros Owzar, Stephen L George

  • 1Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC 27705, USA. jung0005@mc.duke.edu

Statistics in Medicine
|September 29, 2005
PubMed
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This study introduces a novel statistical method for identifying prognostic gene markers in microarray data. The approach effectively handles multiple testing issues, improving the reliability of survival endpoint association analysis.

Area of Science:

  • Genomics
  • Biostatistics
  • Cancer Research

Background:

  • Microarray studies aim to identify prognostic gene markers for survival endpoints like disease recurrence or death.
  • Assessing gene-survival associations requires appropriate measures, statistics, and handling of multiplicity issues.

Purpose of the Study:

  • To develop and evaluate a robust statistical method for identifying prognostic gene markers from large-scale microarray data.
  • To address challenges in assessing gene-survival associations, particularly multiplicity in high-dimensional datasets.

Main Methods:

  • Utilized a general correlation measure and a non-parametric test statistic.
  • Employed permutation resampling to control the family-wise error rate (FWER).
  • Conducted comprehensive simulation studies to assess statistical properties.

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Main Results:

  • The proposed method demonstrated effective handling of multiplicity issues in gene expression analysis.
  • Simulation studies confirmed the statistical properties and reliability of the developed procedure.
  • The method was successfully applied to a lung cancer patient dataset.

Conclusions:

  • The developed statistical approach provides a reliable tool for identifying prognostic gene markers in microarray studies.
  • The method enhances the accuracy of survival endpoint association analysis, particularly in high-dimensional genomic data.
  • This work contributes to advancing biomarker discovery in cancer research.