size_two_arm: Sample size

In npsurvSS: Sample Size and Power Calculation for Common Non-Parametric Tests in Survival Analysis

Description

Calculate required sample size and expected number of events for a two-arm survival study.

Usage

size_two_arm(arm0, arm1, test = list(test = "weighted logrank"),
  power = 0.8, alpha = 0.025, sides = 1)

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_[a]_[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 (in press).
power	1 - type 2 error rate
alpha	type 1 error rate
sides	1=1-sided test, 2=2-sided test

Value

n0	sample size for arm0
n1	sample size for arm1
n	total sample size
d0	expected number of events for arm0
d1	expected number of events for arm1
d	total expected number of events; can be used to convert a time-driven trial to an event-driven trial.

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>

See Also

create_arm for creating an object of class 'arm'.

Examples

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)
size_two_arm(arm0, arm1)
size_two_arm(arm0, arm1, list(test="weighted logrank",
  weight="n",
  mean.approx="generalized schoenfeld",
  var.approx="block"))
size_two_arm(arm0, arm1, list(test="survival difference", milestone=12))
size_two_arm(arm0, arm1, list(test="rmst ratio", milestone=12))
size_two_arm(arm0, arm1, list(test="percentile difference", percentile=0.25))
size_two_arm(arm0, arm1, list(
  list(test="weighted logrank"),
  list(test="survival difference", milestone=12)))

Example output

       n0        n1         n        d0        d1         d 
142.41934 142.41934 284.83868  72.47511  49.72759 122.20270 
       n0        n1         n        d0        d1         d 
149.96628 149.96628 299.93257  76.31563  52.36271 128.67834 
       n0        n1         n        d0        d1         d 
167.78545 167.78545 335.57090  85.38354  58.58450 143.96805 
      n0       n1        n       d0       d1        d 
207.1541 207.1541 414.3083 105.4177  72.3306 177.7483 
       n0        n1         n        d0        d1         d 
274.83583 274.83583 549.67166 139.85991  95.96255 235.82246 
  test       n0       n1        n       d0       d1        d
1    1 142.4193 142.4193 284.8387 72.47511 49.72759 122.2027
2    2 167.7854 167.7854 335.5709 85.38354 58.58450 143.9680

npsurvSS documentation built on Jan. 11, 2020, 9:18 a.m.