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Version: 1.1
Date: 2023-06-22
Title: Cox Regression with Dependent Error in Covariates
Depends: R (≥ 2.8.0)
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Description: Perform the functional modeling methods of Huang and Wang (2018) <doi:10.1111/biom.12741> to accommodate dependent error in covariates of the proportional hazards model. The adopted measurement error model has minimal assumptions on the dependence structure, and an instrumental variable is supposed to be available.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Packaged: 2023-06-23 19:53:59 UTC; eugene
Author: Yijian Huang [aut, cre, cph]
Maintainer: Yijian Huang <yhuang5@emory.edu>
Repository: CRAN
Date/Publication: 2023-06-26 12:00:02 UTC

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)

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They may not be fully stable and should be used with caution. We make no claims about them.
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