# Synthetic Dataset #4: p > n case

### Description

Dataset from simulated regression survival model #4 as described in Dazard et al. (2015).
Here, the regression function uses 1/10 of informative predictors in a *p > n* situation with *p = 1000* and *n = 100*.
The rest represents non-informative noisy covariates, which are not part of the design matrix.
Survival time was generated from an exponential model with rate parameter *λ* (and mean *\frac{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, 2].
In this synthetic example, all covariates are continuous, i.i.d. from a multivariate standard normal distribution.

### Usage

1 |

### Format

Each dataset consists of a `numeric`

`matrix`

containing *n=100* observations (samples)
by rows and *p=1000* variables by columns, not including the censoring indicator and (censored) time-to-event variables.
It comes as a compressed Rda data file.

### Author(s)

"Jean-Eudes Dazard, Ph.D." jxd101@case.edu

"Michael Choe, M.D." mjc206@case.edu

"Michael LeBlanc, Ph.D." mleblanc@fhcrc.org

"Alberto Santana, MBA." ahs4@case.edu

Maintainer: "Jean-Eudes Dazard, Ph.D." jxd101@case.edu

Acknowledgments: 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.

### Source

See simulated survival model #4 in Dazard et al., 2015.

### References

Dazard J-E., Choe M., LeBlanc M. and Rao J.S. (2015). "

*Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods.*" Statistical Analysis and Data Mining (in press).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.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, (in press).Dazard J-E. and J.S. Rao (2010). "

*Local Sparse Bump Hunting.*" J. Comp Graph. Statistics, 19(4):900-92.