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Efficient estimation of grouped survival models.

Zhiguo Li1, Jiaxing Lin2, Alexander B Sibley3

  • 1Department of Biostatistics and Bioinformatics, Duke University, Durham, USA. zhiguo.li@duke.edu.

BMC Bioinformatics
|May 30, 2019
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Summary

This study introduces groupedSurv, an R package for accurate genome-wide analysis of grouped failure time data. It improves precision in analyzing time- and dose-to-event phenotypes, crucial for drug development and genetic studies.

Keywords:
Discrete censoringEfficient scoreGenome-wide analysisGrouped dataHeritabilityMultiple testingPharmacogenomicsScore statistic

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Area of Science:

  • Genomics
  • Biostatistics
  • Pharmacogenomics

Background:

  • Experimental designs often lead to imprecise measurement of time- and dose-to-event data.
  • Grouped failure time data, where events are recorded in discrete intervals, is common in drug toxicity studies.
  • Ignoring this grouping can cause biased results in statistical analyses.

Purpose of the Study:

  • To develop a statistically rigorous and computationally efficient R package for genome-wide analyses using grouped failure time data.
  • To address the limitations of imprecise or incomplete measurement of time- and dose-to-event phenotypes.
  • To enable accurate identification of genetic variants associated with clinical endpoints.

Main Methods:

  • Development of the groupedSurv R package for analyzing grouped failure time phenotypes.
  • Implementation of methods to adjust for baseline covariates.
  • Facilitation of genome-wide association studies at the variant, gene, and pathway levels.

Main Results:

  • The groupedSurv package provides a statistically sound and efficient approach for genome-wide analysis.
  • Simulations demonstrate the statistical properties and computational performance of the package.
  • Reanalysis of a study on taxane-induced peripheral neuropathy identified associated germline variants.

Conclusions:

  • The groupedSurv package enables fast and rigorous genome-wide analysis for grouped failure time data.
  • Analysis can be performed at the variant, gene, or pathway level.
  • The package is publicly available on CRAN.