faft: Fast censored linear regression for the accelerated failure...

In fastAFT: Fast Regression for the Accelerated Failure Time (AFT) Model

Fast censored linear regression for the accelerated failure time (AFT) model

Description

An implementation of the fast censored linear regression in Huang (2013).

Usage

faft(x,dlt,z,weight="logrank",ynci=0,epl=0.95,epu=0.05)

Arguments

x	follow-up time.
dlt	censoring indicator: 1 - event, 0 - censored.
z	matrix of covariates: each column corresponds to a covariate.
weight	either "logrank" or "Gehan" estimating function.
ynci	compute test inversion-based 95% CI's? 1 - yes, 0 - no.
epl	parameter in (0,1) for determining the lower quantile from censored quantile regression (Huang 2010) for the preparatory estimation: sum of squared covariates for at-risk uncensored individuals is about $epl^(dim(z)[2]+1)$ in determinant.
epu	parameter in (0,1) for determining the upper quantile from censored quantile regression (Huang 2010) for the preparatory estimation: sum of squared covariates for at-risk uncensored individuals is about $epu^(dim(z)[2]+1)$ in determinant.

Value

weight	either "logrank" or "Gehan" estimating function.
beta	estimated regression coefficient (the proposed).
va	sandwich variance estimate for beta.
qif	quadratic score statistic at beta.
ci95	test inversion-based 95% CI's, only available if requested and successful.
message	point estimation: "success", "error - algorithm fails", or "warning - singular hessian".
imsg	numerical code for point and test inversion-based interval estimation: 0 - success in point and interval, 1 - error in point where algorithm fails, 2 - warning in point with singular hessian, 3 - success in point but failure in interval.
beta1stp	the one-step estimator.
qif1stp	quadratic score statistic at beta1stp.
betainit	the initial estimator.
qifinit	quadratic score statistic at betainit.

Author(s)

Yijian Huang

References

Huang, Y. (2010) Quantile calculus and censored regression, The Annals of Statistics 38, 1607–1637.

Huang, Y. (2013) Fast censored linear regression. Scandinavian Journal of Statistics 40, 789–806.

Examples

## simulate a dataset of size 100 with 2 covariates
size <- 100
npred <- 2
beta <- rep(1,npred)

cvt <- matrix(rnorm(size*npred),ncol=npred)
resid <- log(rexp(size))
event.t <- resid + cvt %*% beta
censr.t <- log(runif(size, 0, 6))
x <- pmin(event.t, censr.t)
dlt <- as.numeric(event.t<=censr.t)

## run censored linear regression
fit.g <- faft(x,dlt,cvt,weight="Gehan")
fit.l <- faft(x,dlt,cvt,weight="logrank")

fastAFT documentation built on July 9, 2023, 7:05 p.m.