CoxICPen {CoxICPen} | R Documentation |

Perform variable selection for Cox regression model with interval-censored data by using the methods proposed in Zhao et al. (2020a), Wu et al. (2020) and Zhao et al. (2020b). Can deal with both low-dimensional and high-dimensional data.

CoxICPen(LR = LR, x = x, lamb = log(nrow(x))/2-2, beta.initial = rep(0,ncol(x)), pen = "BAR", nfold = 5, BernD = 3, subj.wt = rep(1,nrow(x)))

`LR` |
An n by 2 matrix that contains interval-censored failure times (L, R]. Please set time point R to "Inf" if a subject is right-censored. |

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

`lamb` |
The value of the tuning parameter of the penalty term. Can either be a single value or a vector. Cross-validation will be employed to select the optimal lambda if a vector is provided. Default is log(n)/2-2. |

`beta.initial` |
The initial values for the regression coefficients in the Cox's model. Default is 0. |

`pen` |
The penalty function. Choices include "RIDGE", "BAR", "LASSO", "ALASSO", "SCAD", "MCP", "SICA", "SELO". Default is "BAR". |

`nfold` |
Number of folds for cross-validation. Will be ignored if a single lambda value is provided. Default is 5. |

`BernD` |
The degree of Bernstein polynomials. Default is 3. |

`subj.wt` |
Weight for each subject in the likelihood function. Can be used to incorporate case-cohort design. Default is 1 for each subject. |

beta: Penalized estimates of the regression coefficients in the Cox's model.

phi: Estimates of the coefficients in Bernstein Polynomials.

logL: Log likelihood function based on current parameter estimates and lambda value.

Lamb0: Estimate of the cumulative baseline hazard function at each observation time point.

cv.out: Cross-validation outcome for each lambda. Will be NULL if cross-validation is not performed.

Zhao, H., Wu, Q., Li, G., Sun, J. (2020a). Simultaneous Estimation and Variable Selection for Interval-Censored Data with Broken Adaptive Ridge Regression. Journal of the American Statistical Association. 115(529):204-216.

Wu, Q., Zhao, H., Zhu, L., Sun, J. (2020). Variable Selection for High-dimensional Partly Linear Additive Cox Model with Application to Alzheimer's disease. Statistics in Medicines.39(23):3120-3134.

Zhao, H., Wu, Q., Gilbert, P. B., Chen, Y. Q., Sun, J. (2020b). A Regularized Estimation Approach for Case-cohort Periodic Follow-up Studies with An Application to HIV Vaccine Trials. Biometrical Journal. 62(5):1176-1191.

# Generate an example data require(foreach) n <- 300 # Sample size p <- 20 # Number of covariates bet0 <- c(1, -1, 1, -1, rep(0,p-4)) # True values of regression coefficients set.seed(1) x.example <- matrix(rnorm(n*p,0,1),n,p) # Generate covariates matrix T.example <- c() for (i in 1:n){ T.example[i] <- rexp(1,exp(x.example%*%bet0)[i]) # Generate true failure times } timep <- seq(0,3,,10) LR.example <- c() for (i in 1:n){ obsT <- timep*rbinom(10,1,0.5) if (max(obsT) < T.example[i]) {LR.example <- rbind(LR.example,c(max(obsT), Inf))} else { LR.example <- rbind(LR.example,c(max(obsT[obsT<T.example[i]]), min(obsT[obsT>=T.example[i]]))) } } # Generate interval-censored failure times # Fit Cox's model with penalized estimation model1 <- CoxICPen(LR = LR.example, x = x.example, lamb = 100, pen = "RIDGE") beta.initial <- model1$beta model2 <- CoxICPen(LR = LR.example, x = x.example, beta.initial = beta.initial, pen = "BAR") model2$beta #model3 <- CoxICPen(LR = LR.example, x = x.example, lamb = seq(0.1,1,0.1), # beta.initial = beta.initial, pen = "SELO") #model3$beta

[Package *CoxICPen* version 1.1.0 Index]