op.1stage: Performance evaluation of a one-stage design

In OptOTrials: Optimal Two-Stage Designs for Ordered Categorical Outcomes

Performance evaluation of a one-stage design

Description

This is the function to calculate the operating characteristics for a one-stage design, including type I error, power, and expected sample size.

Usage

op.1stage(alpha, beta, p1, p2, method, n2, t2, nsim = 10000, lambda = 1)

Arguments

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 wi n odds test respectively.
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.

Value

The probability of wrong decision and the expected total sample size under the true hypothesis.

Examples

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)
out <- Decision_rule_W_1stage(p1, p2, alpha, beta, lambda = 1)
# report the power and EN_a
op.1stage(alpha, beta, p1, p2, method="W", n2=out[1], t2=out[2], nsim = 1000, lambda = 1)
# report the overall type I error rate and EN_0
op.1stage(alpha, beta, p1, p1, method="W", n2=out[1], t2=out[2], nsim = 1000, lambda = 1)

OptOTrials documentation built on Sept. 9, 2025, 5:46 p.m.