| 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)