% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phreg_rct.R
\name{phreg_rct}
\alias{phreg_rct}
\title{Lu-Tsiatis More Efficient Log-Rank for Randomized studies with baseline covariates}
\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,
  treat.var = NULL,
  km = TRUE,
  level = 0.95,
  cens.model = NULL,
  estpr = 1,
  pi0 = 0.5,
  base.augment = FALSE,
  return.augmentR0 = FALSE,
  ...
)
}
\arguments{
\item{formula}{formula with 'Surv' or 'Event' outcome (see \code{coxph}) and treatment (randomization 0/1)}

\item{data}{data frame}

\item{cause}{to use for competing risks, recurrent events data}

\item{cens.code}{to use for competing risks, recurrent events data}

\item{typesR}{augmentations used for randomization}

\item{typesC}{augmentations used for censoring}

\item{augmentR0}{formula for the randomization augmentation  (~age+sex)}

\item{augmentR1}{formula for the randomization augmentation  (~age+sex)}

\item{augmentC}{formula for the censoring augmentation  (~age+sex)}

\item{treat.model}{propensity score model, default is ~+1, assuming RCT study}

\item{RCT}{if false will use propensity score adjustment for marginal model}

\item{treat.var}{in case of twostage randomization, this variable is 1 for the treatment times, if start,stop then default assumes that only one treatment at first record}

\item{km}{use Kaplan-Meier for the censoring weights (stratified on treatment)}

\item{level}{of confidence intervals}

\item{cens.model, }{default is censoring model ~strata(treatment) but any model can be used to make censoring martingales}

\item{estpr}{estimates propensity scores}

\item{pi0}{possible fixed propensity scores for randomizations}

\item{base.augment}{TRUE to covariate augment baselines (only for R0 augmentation)}

\item{return.augmentR0}{to return augmentation data}

\item{...}{Additional arguments to phreg function}
}
\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.
}
\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)
}
\references{
Lu, Tsiatis (2008), Improving the efficiency of the log-rank test using auxiliary covariates, Biometrika, 679--694
Scheike et al. (2024), WIP, Two-stage randomization for recurrent events,
}
\author{
Thomas Scheike
}
