truncAIPW {truncAIPW} | R Documentation |
Doubly Robust Estimation under Covariate-induced Dependent Left Truncation and No Censoring
Description
Doubly robust estimation for the mean of an arbitrarily transformed survival time under covariate-induced dependent left truncation and no right censoring.
Usage
truncAIPW(dat, nu, Fuz.mx, Gvz.mx, T.name, Q.name, trim = 1e-07)
Arguments
dat |
data frame that contains the data for constructing the estimating equation. |
nu |
transformation that defines the parameter of interest. |
Fuz.mx |
matrix for the estimated conditional CDF of the event time given covariates. Each row corresponds to a subject, and each column corresponds to a time point. The column names of the matrix are the time points. See |
Gvz.mx |
matrix for the estimated conditional CDF of the truncation time given covariates. Each row corresponds to a subject, and each column corresponds to a time point. The column names of the matrix are the time points. See |
T.name |
name of the event time variable. |
Q.name |
name of the left truncation time variable. |
trim |
constant that is used to bound from below for the denominators involved in the computation. |
Value
truncAIPW()
returns a list of estimators (‘dr’, ‘IPW.Q’, ‘Reg.T1’, ‘Reg.T2’), and the model-based standard errors for the ‘dr’ and ‘IPW.Q’ estimators.
dr |
doubly robust estimator ‘dr’. |
IPW.Q |
inverse probability of truncation weighted estimator ‘IPW.Q’. |
Reg.T1 |
regression based estimator ‘Reg.T1’. |
Reg.T2 |
regression based estimator ‘Reg.T2’. |
SE_dr |
standard error of the ‘dr’ estimator based on the efficient influence function. |
SE_IPW.Q |
standard error of the ‘IPW.Q’ estimator computed from the robust sandwich variance estimator assuming the truncation weights are known. |
References
Wang, Y., Ying, A., Xu, R. (2022) "Doubly robust estimation under covariate-induced dependent left truncation" <arXiv:2208.06836>.
See Also
See truncAIPW_cen1
, truncAIPW_cen2
for the estimations also under noninformative right censoring. See F_est
, G_est
for examples of computing the input matrices for the conditional CDF's.
Examples
data("simu")
nu <- function(t){ return(as.numeric(t>3)) }
u = c(min(simu$time)-1e-10, sort(simu$time), max(simu$time)+1e-10)
v = c(min(simu$Q)-1e-10, sort(simu$Q), max(simu$Q)+1e-10)
Fuz.mx = F_est(simu, simu, u, "Cox", "time", "Q", "delta", c("Z1","Z2"))
Gvz.mx = G_est(simu, simu, v, "Cox", "time", "Q", "delta", c("Z1","Z2"))
est = truncAIPW(simu, nu, Fuz.mx, Gvz.mx, "time", "Q", trim = 1e-7)
est