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Lu-Tsiatis More Efficient Log-Rank for Randomized studies with baseline covariates

Source: R/phreg_rct.R
phreg_rct.Rd

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"),
  weights = NULL,
  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,
  mlogit = FALSE,
  ...
)

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

weights

weights for score equation

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 an RCT study

RCT

if false will use propensity score adjustment for marginal model

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

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 propensity scores for randomizations

base.augment

TRUE to covariate augment baselines (only for R0 augmentation)

return.augmentR0

to return augmentation data

mlogit

if TRUE then forces use of this function for propensity scores, default for binary treatment is glm

...

Additional arguments to phreg function

References

Lu, Tsiatis (2008), Improving the efficiency of the log-rank test using auxiliary covariates, Biometrika, 679–694

Scheike, Nerstroem and Martinussen (2025), Randomized clinical trials and the proportional hazards model for recurrent events.

Author

Thomas Scheike

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)
#>                 Estimate   Std.Err       2.5%     97.5%   P-value
#> Marginal-Z.f1  0.1781155 0.2639114 -0.3391412 0.6953723 0.4997350
#> R0_C:Z.f1     -0.0175242 0.2071406 -0.4235122 0.3884638 0.9325790
#> R0_dynC:Z.f1   0.1109733 0.2022974 -0.2855223 0.5074689 0.5833039
#> attr(,"class")
#> [1] "summary.phreg_rct"