Correlation of PFS and OS event times for data from the IDM

Usage

corPFSOS(
  data,
  transition,
  bootstrap = TRUE,
  bootstrap_n = 100,
  conf_level = 0.95
)

Arguments

data: (data.frame)
in the format produced by getOneClinicalTrial().
transition: (TransitionParameters object)
specifying the assumed distribution of transition hazards. Initial parameters for optimization can be specified here. See exponential_transition() or weibull_transition() for details.
bootstrap: (flag)
if TRUE computes confidence interval via bootstrap.
bootstrap_n: (count)
number of bootstrap samples.
conf_level: (proportion)
confidence level for the confidence interval.

Value

The correlation of PFS and OS.

Examples

transition <- exponential_transition(h01 = 1.2, h02 = 1.5, h12 = 1.6)
data <- getClinicalTrials(
  nRep = 1, nPat = c(100), seed = 1234, datType = "1rowTransition",
  transitionByArm = list(transition), dropout = list(rate = 0.5, time = 12),
  accrual = list(param = "intensity", value = 7)
)[[1]]
#> Simulating 1 trials:
#> 
  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
corPFSOS(data, transition = exponential_transition(), bootstrap = FALSE)
#> $corPFSOS
#> [1] 0.6015421
#> 
if (FALSE) {
corPFSOS(data, transition = exponential_transition(), bootstrap = TRUE)
}