Description Usage Arguments Details Value References Examples

This function returns a classification (decision) tree for a given survival dataset. The decision of making inner nodes (splitting criterion) is based on the univariate score tests. The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument. This tree construction algorithm is proposed by Emura et al. (2021).

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`t.vec` |
:Vector of survival times (time to either death or censoring) |

`d.vec` |
:Vector of censoring indicators (1=death, 0=censoring) |

`X.mat` |
:n by p matrix of covariates (features), where n is the sample size and p is the number of covariates |

`P.value` |
:the threshold of P-value for stop splitting (stopping criterion) |

`d0` |
:A positive constant to stabilize the variance of score statistics (Witten & Tibshirani 2010) |

`S.plot` |
:call for plot the KM estimator for each split |

`score` |
:TRUE = score test (Emura et al. 2019); FALSE = log-rank test |

In order to stabilize the univariate score tests, a small value "d0" is added to the variance of the score statistics (Witten and Tibshirani 2010). d0=0 corresponds to the logrank test. To perform a large number of the score tests, the "compound.Cox" packages (Emura et al.2019) is applied with d0 as a option.

A nested list describing a classification tree, consisting of inner nodes and terminal node.

Emura T, Hsu WC, Chou WC (2021). A survival tree based on stabilized score tests for high-dimensional covariates, in review

Emura T, Matsui S, Chen HY (2019). compound.Cox: Univariate Feature Selection and Compound Covariate for Predicting Survival, Computer Methods and Programs in Biomedicine 168: 21-37.

Witten DM, Tibshirani R (2010) Survival analysis with high-dimensional covariates. Stat Method Med Res 19:29-51

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