phreg_rct: Lu-Tsiatis More Efficient Log-Rank for Randomized studies...

In mets: Analysis of Multivariate Event Times

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 coxph) and treatment (randomization 0/1)
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)

mets documentation built on May 29, 2024, 3:51 a.m.