Description Usage Arguments Details Value References Author(s) Examples

Cross-validation for selecting the number of binary rules in interaction AIM with survival outcomes in the context of Cox regression.

1 | ```
cv.cox.interaction(x, trt, y, status, K.cv=5, num.replicate=1, nsteps, mincut=0.1, backfit=F, maxnumcut=1, dirp=0)
``` |

`x` |
n by p matrix. The covariate matrix |

`trt` |
n vector. The treatment indicator |

`y` |
n vector. The observed follow-up time |

`status` |
n 0/1 vector. The status indicator. 1=failure and 0=alive. |

`K.cv` |
K.cv-fold cross validation |

`num.replicate` |
number of independent replications of K-fold cross validations. |

`nsteps` |
the maximum number of binary rules to be included in the index |

`backfit` |
T/F. Whether the existing split points are adjusted after including a new binary rule |

`mincut` |
the minimum cutting proportion for the binary rule at either end. It typically is between 0 and 0.2. |

`maxnumcut` |
the maximum number of binary splits per predictor |

`dirp` |
p vector. The given direction of the binary split for each of the p predictors. 0 represents "no pre-given direction"; 1 represents "(x>cut)"; -1 represents "(x<cut)". Alternatively, "dirp=0" represents that there is no pre-given direction for any of the predictor. |

`cv.cox.interaction`

implements K-fold cross-validation for the interaction Cox AIM. It estimates the partial likelihood score test statistics for testing the treatment*index interaction in the test set. It also provides pre-validated fits for each observation and pre-validated partial likelihood score test statistics. The output can be used to select the optimal number of binary rules.

`cv.cox.interaction`

returns

`kmax` |
the optimal number of binary rules based the cross-validation |

`meanscore` |
nsteps-vector. The cross-validated partial likelihood score test statistics (significant at 0.05, if greater than 1.96) for the treatment*index interaction. |

`pvfit.score` |
nsteps-vector. The pre-validated partial likelihood score test statistics (significant at 0.05, if greater than 1.96) for the treatment*index interaction. |

`preval` |
nsteps by n matrix. Pre-validated fits for individual observation |

L Tian and R Tibshirani Adaptive index models for marker-based risk stratification, Tech Report, available at http://www-stat.stanford.edu/~tibs/AIM.

R Tibshirani and B Efron, Pre-validation and inference in microarrays, Statist. Appl. Genet. Mol. Biol., 1:1-18, 2002.

Lu Tian and Robert Tibshirani

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
## generate data
set.seed(1)
n=400
p=10
x=matrix(rnorm(n*p), n, p)
z=(x[,1]<0.2)+(x[,5]>0.2)
beta=1
trt=rbinom(n,1,0.5)
fail.time=rexp(n)*exp(-beta*z*trt)
cen.time=rexp(n)*1.25
y=pmin(fail.time, cen.time)
y=round(y*10)/10
delta=1*(fail.time<cen.time)
## cross-validate the interaction Cox AIM model
a=cv.cox.interaction(x, trt, y, delta, nsteps=10, K.cv=4, num.replicate=5)
## examine the score test statistics for the interaction in the test set
par(mfrow=c(1,2))
plot(a$meanscore, type="l")
plot(a$pvfit.score, type="l")
## construct the index with the optimal number of binary rules
k.opt=a$kmax
a=cox.interaction(x, trt, y, delta, nsteps=k.opt)
print(a)
``` |

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