SurrogateSeq (version 1.1)

gs.boundaries: Computes group sequential boundaries

Description

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).

Usage

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)

Value

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

Arguments

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

Author

Layla Parast and Jay Bartroff

References

Parast and Bartroff (2024). Group sequential testing of a treatment effect using a surrogate marker. Biometrics, 80(4), ujae108.

Examples

data(example.data)
data(StudyA.aids)
data(StudyB.aids)
s0.studya = StudyA.aids$s0
s1.studya = StudyA.aids$s1

# \donttest{
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, plot=TRUE)

bound
# }