gsdesign {clinfun} | R Documentation |
Group Sequential Designs
Description
Functions to calculate sample size for group sequential designs
Usage
gsdesign.binomial(ifrac, pC, pE, sig.level = 0.05, power = 0.8,
delta.eb=0.5, delta.fb = 0, alternative = c("two.sided", "one.sided"),
tol=0.0001)
gsdesign.normal(ifrac, delta, sd = 1, sig.level = 0.05, power = 0.8,
delta.eb = 0.5, delta.fb = 0, alternative = c("two.sided", "one.sided"),
tol=0.0001)
gsdesign.survival(ifrac, haz.ratio, sig.level = 0.05, power = 0.8,
delta.eb = 0.5, delta.fb = 0, alternative = c("two.sided", "one.sided"),
tol=0.0001)
Arguments
ifrac |
information fraction ratio of current to total sample
size of number of events. |
pC |
prob of success of the standard therapy (for binomial data) |
pE |
prob of success of the experimental therapy (for binomial data) |
delta |
true difference in means (for normal data) |
sd |
standard deviation (for normal data) |
haz.ratio |
hazard ratio (for survival comarison) |
sig.level |
significance level (type I error probability) |
power |
power of test (1 minus type II error probability) |
delta.eb |
power for efficacy boundary in the Pocock (power=0) to
O'Brien-Fleming (power=0.5) family (default is 0.5) |
delta.fb |
power for futility boundary in the Pocock (power=0) to
O'Brien-Fleming (power=0.5) family (default is 0.5) |
alternative |
one- or two-sided test. |
tol |
tolerance level for multivariate normal probability
computation. |
Value
a list with ifrac, sig.level, power, alternative, delta.eb and:
efbdry |
the critical value to use at the different looks. |
sample.size |
the sample size per arm for binomial/normal data. |
num.events |
the total number of failures which should be
converted to number of subjects using censoring proportion. |
[Package
clinfun version 0.7-3
Index]