phreg_rct {mets} | R Documentation |
Lu-Tsiatis More Efficient Log-Rank for Randomized studies with baseline covariates
Description
Efficient implementation of the Lu-Tsiatis improvement using baseline covariates, extended to competing risks and recurrent events. Results almost equivalent with the speffSurv function of the speff2trial function in the survival case. A dynamic censoring augmentation regression is also computed to gain even more from the censoring augmentation. Furhter, we also deal with twostage randomizations. The function was implemented to deal with recurrent events (start,stop) + cluster, and more examples in vignette.
Usage
phreg_rct(
formula,
data,
cause = 1,
cens.code = 0,
typesR = c("R0", "R1", "R01"),
typesC = c("C", "dynC"),
augmentR0 = NULL,
augmentR1 = NULL,
augmentC = NULL,
treat.model = ~+1,
RCT = TRUE,
weight.var = NULL,
km = TRUE,
level = 0.95,
cens.model = NULL,
estpr = 1,
pi0 = 0.5,
...
)
Arguments
formula |
formula with 'Surv' or 'Event' outcome (see |
data |
data frame |
cause |
to use for competing risks, recurrent events data |
cens.code |
to use for competing risks, recurrent events data |
typesR |
augmentations used for randomization |
typesC |
augmentations used for censoring |
augmentR0 |
formula for the randomization augmentation (~age+sex) |
augmentR1 |
formula for the randomization augmentation (~age+sex) |
augmentC |
formula for the censoring augmentation (~age+sex) |
treat.model |
propensity score model, default is ~+1, assuming RCT study |
RCT |
if false will use propensity score adjustment |
weight.var |
in case of twostage randomization, this variable is 1 for the treatment times, |
km |
use Kaplan-Meier for the censoring weights (stratified on treatment) |
level |
of confidence intervals |
cens.model |
default is censoring model ~strata(treatment) but any model can be used to make censoring martingales |
estpr |
estimates propensity scores |
pi0 |
possible fixed propoensity scores for randomizations |
... |
Additional arguments to phreg function |
Author(s)
Thomas Scheike
References
Lu, Tsiatis (2008), Improving the efficiency of the log-rank test using auxiliary covariates, Biometrika, 679–694 Scheike (2024), WIP, Two-stage randomization for recurrent events,
Examples
## Lu, Tsiatis simulation
data <- mets:::simLT(0.7,100)
dfactor(data) <- Z.f~Z
out <- phreg_rct(Surv(time,status)~Z.f,data=data,augmentR0=~X,augmentC=~factor(Z):X)
summary(out)