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Efficient estimation of grouped survival models.

Publication ,  Journal Article
Li, Z; Lin, J; Sibley, AB; Truong, T; Chua, KC; Jiang, Y; McCarthy, J; Kroetz, DL; Allen, A; Owzar, K
Published in: BMC Bioinformatics
May 28, 2019

BACKGROUND: Time- and dose-to-event phenotypes used in basic science and translational studies are commonly measured imprecisely or incompletely due to limitations of the experimental design or data collection schema. For example, drug-induced toxicities are not reported by the actual time or dose triggering the event, but rather are inferred from the cycle or dose to which the event is attributed. This exemplifies a prevalent type of imprecise measurement called grouped failure time, where times or doses are restricted to discrete increments. Failure to appropriately account for the grouped nature of the data, when present, may lead to biased analyses. RESULTS: We present groupedSurv, an R package which implements a statistically rigorous and computationally efficient approach for conducting genome-wide analyses based on grouped failure time phenotypes. Our approach accommodates adjustments for baseline covariates, and analysis at the variant or gene level. We illustrate the statistical properties of the approach and computational performance of the package by simulation. We present the results of a reanalysis of a published genome-wide study to identify common germline variants associated with the risk of taxane-induced peripheral neuropathy in breast cancer patients. CONCLUSIONS: groupedSurv enables fast and rigorous genome-wide analysis on the basis of grouped failure time phenotypes at the variant, gene or pathway level. The package is freely available under a public license through the Comprehensive R Archive Network.

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Published In

BMC Bioinformatics

DOI

EISSN

1471-2105

Publication Date

May 28, 2019

Volume

20

Issue

1

Start / End Page

269

Location

England

Related Subject Headings

  • Statistics as Topic
  • Software
  • Phenotype
  • Models, Genetic
  • Likelihood Functions
  • Humans
  • Genome-Wide Association Study
  • Gene Frequency
  • Bioinformatics
  • Benchmarking
 

Citation

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MLA
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Li, Z., Lin, J., Sibley, A. B., Truong, T., Chua, K. C., Jiang, Y., … Owzar, K. (2019). Efficient estimation of grouped survival models. BMC Bioinformatics, 20(1), 269. https://doi.org/10.1186/s12859-019-2899-x
Li, Zhiguo, Jiaxing Lin, Alexander B. Sibley, Tracy Truong, Katherina C. Chua, Yu Jiang, Janice McCarthy, Deanna L. Kroetz, Andrew Allen, and Kouros Owzar. “Efficient estimation of grouped survival models.BMC Bioinformatics 20, no. 1 (May 28, 2019): 269. https://doi.org/10.1186/s12859-019-2899-x.
Li Z, Lin J, Sibley AB, Truong T, Chua KC, Jiang Y, et al. Efficient estimation of grouped survival models. BMC Bioinformatics. 2019 May 28;20(1):269.
Li, Zhiguo, et al. “Efficient estimation of grouped survival models.BMC Bioinformatics, vol. 20, no. 1, May 2019, p. 269. Pubmed, doi:10.1186/s12859-019-2899-x.
Li Z, Lin J, Sibley AB, Truong T, Chua KC, Jiang Y, McCarthy J, Kroetz DL, Allen A, Owzar K. Efficient estimation of grouped survival models. BMC Bioinformatics. 2019 May 28;20(1):269.
Journal cover image

Published In

BMC Bioinformatics

DOI

EISSN

1471-2105

Publication Date

May 28, 2019

Volume

20

Issue

1

Start / End Page

269

Location

England

Related Subject Headings

  • Statistics as Topic
  • Software
  • Phenotype
  • Models, Genetic
  • Likelihood Functions
  • Humans
  • Genome-Wide Association Study
  • Gene Frequency
  • Bioinformatics
  • Benchmarking