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 or the ratio of current sample size
or number of events to the total sample size or number of events.
This should be an increasing vector of numbers from 0 to 1 with the
last one being 1. If just 1 is given a fixed sample design is derived. |
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 comparison) |
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 0.8.4
Index]