fastAFT (version 1.4)

faft: 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)

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.

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.

Author

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")