op.FS | R Documentation |
This is the function to calculate the operating characteristics for the FS design, including type I error, power, and expected sample size.
op.FS(alpha, beta, p1, p2, method, n1, t1l, t1u, n2, t2, nsim = 10000, lambda = 1)
alpha |
Target type I error rate. |
beta |
Target type II error rate. |
p1 |
A vector containing the probabilities of the outcome falling into each level of the control arm. |
p2 |
A vector containging the probabilities of the outcome falling into each level of the control arm. |
method |
"S", "M" or "W", denotes score test, Mann-Whitney-Wilcoxon test and win odds test respectively. |
n1 |
The total sample size of the control and experimental groups required at the 1st analysis. |
t1l |
The lower threshold of the test statistic at the 1st analysis. |
t1u |
The upper threshold of the test statistic at the 1st analysis. |
n2 |
The cumulative total sample size of the control and experimental groups required at the 2nd analysis. |
t2 |
The threshold of the test statistic at the 2nd analysis. |
nsim |
The number of simulations. nsim = 10000 by default |
lambda |
The ratio of sample sizes between the experimental and control groups, defined as sample size (experimental): sample size (control) = lambda:1. The default value is 1. |
The probability of wrong decision and the expected total sample size under the true hypothesis.
set.seed(1234)
alpha = 0.05; beta = 0.2;
p1 = c(0.2, 0.5, 0.2, 0.1)
p2 = c(0.4, 0.3, 0.2, 0.1)
alpha1 <- 0.2
alpha2 <- 0.025
beta1 <- 0.1
out <- Decision_rule_W.FS(p1, p2, alpha1, alpha2, beta1, alpha, beta, lambda = 1)
# heavier example for illustration (skipped on CRAN timing checkes)
# report the power and EN_a
op.FS(alpha, beta, p1, p2, method="W", n1=out[1], t1l=out[2],
t1u=out[3], n2=out[4], t2=out[5], nsim = 10000, lambda = 1)
# report the overall type I error rate and EN_0
op.FS(alpha, beta, p1, p1, method="W", n1=out[1], t1l=out[2],
t1u=out[3], n2=out[4], t2=out[5], nsim = 10000, lambda = 1)
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