Synthetic.1-data: Synthetic Dataset #1: p < n case

Synthetic.1R Documentation

Synthetic Dataset #1: p < n case

Description

Dataset from simulated regression survival model #1 as described in Dazard et al. (2015). Here, the regression function uses all of the predictors, which are also part of the design matrix. Survival time was generated from an exponential model with rate parameter λ (and mean 1/λ) according to a Cox-PH model with hazard exp(eta), where eta(.) is the regression function. Censoring indicator were generated from a uniform distribution on [0, 3]. In this synthetic example, all covariates are continuous, i.i.d. from a multivariate uniform distribution on [0, 1].

Usage

    data("Synthetic.1", package="PRIMsrc")

Format

Each dataset consists of a numeric matrix containing n=250 observations (samples) by rows and p=3 variables by columns, not including the censoring indicator and (censored) time-to-event variables. It comes as a compressed Rda data file.

Acknowledgments

This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University. This project was partially funded by the National Institutes of Health NIH - National Cancer Institute (R01-CA160593) to J-E. Dazard and J.S. Rao.

Author(s)

Maintainer: "Jean-Eudes Dazard, Ph.D." jean-eudes.dazard@case.edu

Source

See simulated survival model #1 in Dazard et al., 2015.

References

  • Dazard J-E. and Rao J.S. (2021a). "Variable Selection Strategies for High-Dimensional Recursive Peeling-Based Survival Bump Hunting Models." (in prep).

  • Dazard J-E. and Rao J.S. (2021b). "Group Bump Hunting by Recursive Peeling-Based Methods: Application to Survival/Risk Predictive Models." (in prep).

  • Dazard J-E., Choe M., Pawitan Y., and Rao J.S. (2021c). "Identification and Characterization of Informative Prognostic Subgroups by Survival Bump Hunting." (in prep).

  • Rao J.S., Huilin Y., and Dazard J-E. (2020). "Disparity Subtyping: Bringing Precision Medicine Closer to Disparity Science." Cancer Epidemiology Biomarkers & Prevention, 29(6 Suppl):C018.

  • Yi C. and Huang J. (2017). "Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression." J. Comp Graph. Statistics, 26(3):547-557.

  • Dazard J-E., Choe M., LeBlanc M., and Rao J.S. (2016). "Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods." Statistical Analysis and Data Mining, 9(1):12-42.

  • Dazard J-E., Choe M., LeBlanc M., and Rao J.S. (2015). "R package PRIMsrc: Bump Hunting by Patient Rule Induction Method for Survival, Regression and Classification." In JSM Proceedings, Statistical Programmers and Analysts Section. Seattle, WA, USA. American Statistical Association IMS - JSM, p. 650-664.

  • Dazard J-E., Choe M., LeBlanc M., and Rao J.S. (2014). "Cross-Validation of Survival Bump Hunting by Recursive Peeling Methods." In JSM Proceedings, Survival Methods for Risk Estimation/Prediction Section. Boston, MA, USA. American Statistical Association IMS - JSM, p. 3366-3380.


jedazard/PRIMsrc documentation built on July 16, 2022, 10:56 p.m.