View source: R/SurrogateSeq.functions.R
gs.boundaries | R Documentation |
Computes group sequential (and naive) boundaries for the nonparametric test for a treatment effect on the primary outcome using surrogate marker information. The boundaries and test statistic borrow information from a prior study (Study A) about the relationship between the surrogate and the primary outcome to test for a treatment effect in the current study (Study B).
gs.boundaries(szerop, sonep, yzerop, nzero, none, n.stg, B.norm = 1e+06,
alpha = 0.05, pp = 0.4, inf.fraction = (1:n.stg)/n.stg, plot=FALSE)
szerop |
surrogate marker in the control group in Study A |
sonep |
surrogate marker in the treated group in Study A |
yzerop |
primary outcome in the control group in Study A |
nzero |
sample size of control group in Study B |
none |
sample size of treated group in Study B |
n.stg |
maximum number of analyses |
B.norm |
number of multivariate normal vectors to use in simulation for boundaries; default is 1e+06 |
alpha |
desired rejection probability of the test; default is 0.05 |
pp |
power parameter for Wang-Tsiatis boundaries; default is 0.4 |
inf.fraction |
information fraction vector of the same length as n.stg which reflects the fraction of information at each analysis, should be positive, non-decreasing, and the last entry should be 1; default is (1:n.stg)/n.stg, user may want to specify a different vector for unequal time points |
plot |
TRUE or FALSE if a plot of the boundaries is desired; default is FALSE |
Returns a list of boundaries:
Naive |
Naive boundaries |
Bonf |
Bonferroni boundaries |
Pocock |
Pocock boundaries |
OBrien_Fleming |
O'Brien-Fleming boundaries |
Wang_Tsiatis |
Wang-Tsiatis boundaries |
Layla Parast and Jay Bartroff
Parast and Bartroff (2024). Group sequential testing of a treatment effect using a surrogate marker. Biometrics, 80(4), ujae108.
data(example.data)
data(StudyA.aids)
data(StudyB.aids)
s0.studya = StudyA.aids$s0
s1.studya = StudyA.aids$s1
bound = gs.boundaries(szerop = s0.studya, sonep = s1.studya, yzerop=StudyA.aids$y0,
nzero = nrow(StudyB.aids$s0),none = nrow(StudyB.aids$s1), n.stg=4, B.norm=1e6,
alpha=0.05)
bound
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