Boosting: Estimation of Functional Forms of Covaraites under AFT Models

In AFFECT: Accelerated Functional Failure Time Model with Error-Contaminated Survival Times

Estimation of Functional Forms of Covaraites under AFT Models

Description

The function aims to select informative covariates under the AFT model and estimate their corresponding functional forms with survival time. Specifically, the first step in this function is to derive an unbiased estimating function by the Buckley-James method with corrected survival times and censoring status. After that, a boosting algorithm with the cubic-spline method is implemented to an unbiased estimating function to detect informative covariates and estimate the functional forms of covariates iteratively.

Usage

Boosting(data, iter = 50)

Arguments

data	A c(n,p+2) dimension of data, where n is sample size and p is the number of covariates. The first column is survival time and second column is censoring status, and the other columns are covariates.
iter	The iteration times of the boosting procedure. The default value = 50 and the iteration will stop when the absolute value of increment of every estimated value is small than 0.01.

Value

covariates The first ten covariates that are selected in the iteration.

functional_forms The functional forms of the first ten covariates that are selected in the iteration.

predicted_failure_time The predicted failure time of every sample

survival_curve Predicted survival curve of the sample.

Examples

## generate data with misclassification = 0.9 with n = 50, p = 6
## and variance of noise term is 0.75. The y* is is related to the first
## covariate.

b <- matrix(0,ncol=6, nrow = 1)
b[1,1] <- 1
data <- data_gen(n=50, p=6, pi_01=0.9, pi_10 = 0.9, gamma0=1,
gamma1=b, e_var=0.75)

## Assume that covariates are independent and observed failure time is
## related to first covariate with weight equals 1. And the scalar
## in the classical additive measurement error model is 1 and
## Misclassifcation probability = 0.9.

matrixb <- diag(6)
gamma_0 <-  1
gamma_1 <- matrix(0,ncol=6, nrow =1)
gamma_1[1,1] <- 1
data1 <- ME_correction(pi_10=0.9,pi_01=0.9,gamma0 = gamma_0,
gamma1 = gamma_1,
cor_covar=matrixb, y=data[,1],
indicator=data[,2], covariate = data[,3:8])
data1 <- cbind(data1,data[,3:8])

## Data in boosting procedure with iteration times =2

result <- Boosting(data=data1, iter=2)

AFFECT documentation built on July 9, 2023, 6:45 p.m.