coxerr {coxerr}

Cox regression with dependent error in covariates

Description

Estimation methods of Huang and Wang (2018)

Usage

coxerr(t,dlt,wuz,method,initbt=rep(0,dim(as.matrix(wuz))[2]-1),
       derr=1e-6)

Arguments

t

follow-up time.

dlt

censoring indicator: 1 - event, 0 - censored.

wuz

covariate-related variables: wuz[,1] - mismeasured, wuz[,2] - instrumental variable (IV), wuz[,-c(1,2)] - accurately measured.

method

estimation method: 1 - Prop1, 2 - Prop 2.

initbt

initial value for the estimate.

derr

error tolerance.

Value

bt

point estimate.

va

estimated variance-covariance matrix.

succ

indicator for estimate-finding success.

Author(s)

Yijian Huang

References

Huang, Y. and Wang, C. Y. (2018) Cox Regression with dependent error in covariates, Biometrics 74, 118–126.

Examples

## simulate a dataset following Scenario 1 of Table 1 in Huang and Wang (2018)
size <- 300
bt0 <- 1

## true covariate
x <- rnorm(size)

## survival time, censoring time, follow-up time, censoring indicator
s <- rexp(size) * exp(-bt0 * x)
c <- runif(size) * ifelse(x <= 0, 4.3, 8.6)
t <- pmin(s, c)
dlt <- as.numeric(s <= c)

## mismeasured covariate with heterogeneous error, IV
w <- x + rnorm(size) * sqrt(pnorm(x) * 2) * 0.5 + 1
u <- x * 0.8 + rnorm(size) * 0.6
wuz <- cbind(w, u)

## estimation using PROP1
fit1 <- coxerr(t, dlt, wuz, 1)
## estimation using PROP2
fit2 <- coxerr(t, dlt, wuz, 2)