Implements a modification to the Random Survival Forests algorithm [1] for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests [2]. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic [3].

Package: | icRSF |

Type: | Package |

Version: | 1.0 |

Date: | 2016-01-01 |

License: | GPL (>= 2) |

Hui Xu and Raji Balasubramanian

Maintainer: Hui Xu <huix@schoolph.umass.edu> and Raji Balasubramanian <rbalasub@schoolph.umass.edu>

[1] Ishwaran, H. and Kogalur, U. B. and Blackstone, E. H. and Lauer, M. S. (2008). Random Survival Forests, Annals of Applied Statistics, Vol.2, Number 3, pp. 841-860. <DOI: 10.1214/08-AOAS169>.

[2] Gu, X., Ma, Y., Balasubramanian, R. (2015). Semi-parametric time to event models in the presence of error-prone, self-reported outcomes - with application to the Women's Health Initiative, Annals of Applied Statistics, 9 (2), 714-730. <DOI: 10.1214/15-AOAS810>

[3] Xu, H., Gu, X., Tadesse, M. G., Balasubramanian, R. (2016). A modified Random Survival Forests algorithm for variable selection in the presence of imperfect self-reports or laboratory based diagnostic tests, Submitted to Journal of Computational and Graphical Statistics.

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