Efficient estimation of grouped survival models
| Autor: | Janice M. McCarthy, Tracy Truong, Jiaxing Lin, Yu Jiang, Deanna L. Kroetz, Kouros Owzar, Alexander B. Sibley, Andrew S. Allen, Zhiguo Li, Katherina C. Chua |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Statistics as Topic Efficient score computer.software_genre Biochemistry Mathematical Sciences 0302 clinical medicine Gene Frequency Models Genome-wide analysis Structural Biology Multiple testing lcsh:QH301-705.5 Cancer Likelihood Functions 0303 health sciences Applied Mathematics Grouped data Biological Sciences 3. Good health Computer Science Applications Benchmarking Phenotype 030220 oncology & carcinogenesis lcsh:R858-859.7 Data mining Bioinformatics lcsh:Computer applications to medicine. Medical informatics Heritability 03 medical and health sciences Genetic Information and Computing Sciences Breast Cancer Covariate Genetics Humans Molecular Biology Survival analysis 030304 developmental biology Data collection Models Genetic Human Genome Score statistic lcsh:Biology (General) Discrete censoring Multiple comparisons problem Pharmacogenomics computer Software Genome-Wide Association Study |
| Zdroj: | BMC Bioinformatics, Vol 20, Iss 1, Pp 1-11 (2019) BMC bioinformatics, vol 20, iss 1 BMC Bioinformatics |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/s12859-019-2899-x |
| Popis: | 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. Electronic supplementary material The online version of this article (10.1186/s12859-019-2899-x) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink