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Group Tests for High-dimensional Failure Time Data with the Additive Hazards Models.

Dandan Jiang1, Jianguo Sun1

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The International Journal of Biostatistics
|May 12, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for identifying disease-related genes using high-dimensional genetic data. The corrected variance reduced partial profiling (CVRPP) model offers improved accuracy in analyzing genetic associations with diseases.

Keywords:
additive hazards modelgroup testshigh-dimensional data

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Area of Science:

  • Statistics
  • Genomics
  • Biostatistics

Background:

  • High-dimensional data analysis is crucial in genomics.
  • Identifying genes associated with diseases is a key challenge.

Purpose of the Study:

  • To develop a statistical method for identifying genes related to diseases using right-censored failure time data.
  • To address the problem of group testing in the context of genetic association studies.

Main Methods:

  • Developed a corrected variance reduced partial profiling (CVRPP) linear regression model.
  • Proposed a likelihood ratio test procedure for the additive hazards model.
  • Utilized statistical analysis for high-dimensional genetic data.

Main Results:

  • The CVRPP model demonstrates good performance in practical scenarios.
  • The proposed method shows superior performance compared to existing approaches.
  • Effective identification of significant genes related to disease occurrence.

Conclusions:

  • The CVRPP linear regression model and likelihood ratio test are effective for analyzing high-dimensional genetic data.
  • The developed methods provide a valuable tool for disease gene identification in genomics.
  • The study offers a robust approach for statistical analysis in genetic studies.