power_two_arm: Power

Description

Calculate power for a two-arm survival study.

Usage

power_two_arm(arm0, arm1, test = list(test = "weighted logrank"),
  alpha = 0.025, sides = 1)

Value

power.

Arguments

arm0: object of class 'arm'.
arm1: object of class 'arm'.
test: list or list of lists. Each list must contain at minimum the key 'test' describing the type of statistical test. Default test is the "weighted logrank". Kaplan-Meier based tests ("survival difference", "survival ratio", "rmst difference", "rmst ratio", "percentile difference", and "percentile ratio") require the user to define an additional key, either the desired 'milestone' or 'percentile'. The weighted log-rank test does not require additional keys. However, user may choose which weight function ("1"=unweighted, "n"=Gehan-Breslow, "sqrtN"=Tarone-Ware, "FH_p[a]_q[b]"= Fleming-Harrington with p=a and q=b) and which approximation for the large-sample mean ("asymptotic", "generalized schoenfeld", "event driven") and variance ("1", "block[ randomization]", "simple[ randomization]") they wish to use. Default choice is 'weight'="1", 'mean.approx'="asymptotic", and 'var.approx'="1". For more details regarding the different mean and variance approximations for the weight log-rank test, please see Yung and Liu (2019).
alpha: type 1 error rate
sides: 1=1-sided test, 2=2-sided test

References

Yung, G and Liu, Y. (2019). Sample size and power for the weighted log-rank test and Kaplan-Meier based tests with allowance for non-proportional hazards. Biometrics. <doi:10.1111/biom.13196>

Examples

Run this code

arm0 <- create_arm(size=120, accr_time=6, surv_scale=0.05, loss_scale=0.005, follow_time=12)
arm1 <- create_arm(size=120, accr_time=6, surv_scale=0.03, loss_scale=0.005, follow_time=12)
power_two_arm(arm0, arm1)
power_two_arm(arm0, arm1, list(test="weighted logrank",
  weight="n",
  mean.approx="generalized schoenfeld",
  var.approx="block"))
power_two_arm(arm0, arm1, list(test="survival difference", milestone=12))
power_two_arm(arm0, arm1, list(test="rmst ratio", milestone=12))
power_two_arm(arm0, arm1, list(test="percentile difference", percentile=0.25))
power_two_arm(arm0, arm1, list(
  list(test="weighted logrank"),
  list(test="survival difference", milestone=12)))