The function determines (approximately) the boundaries of a multi-arm multi-stage study with ordinal or binary endpoints for a given boundary shape and finds the required number of subjects.
ordinal.mams(
prob = c(0.35, 0.4, 0.25),
or = 2,
or0 = 1.2,
K = 4,
J = 2,
alpha = 0.05,
power = 0.9,
r = 1:2,
r0 = 1:2,
ushape = "obf",
lshape = "fixed",
ufix = NULL,
lfix = 0,
nstart = 1,
nstop = NULL,
sample.size = TRUE,
Q = 20,
parallel = TRUE,
print = TRUE
)An object of the class MAMS containing the following components:
Vector of expected probabilities of falling into each category
under control conditions. The elements must sum up to one
(default=c(0.35, 0.4, 0.25)).
Interesting treatment effect on the scale of odds ratios
(default=2).
Uninteresting treatment effect on the scale of odds ratios
(default=1.2).
Number of experimental treatments (default=4).
Number of stages (default=2).
One-sided familywise error rate (default=0.05).
Desired power (default=0.9).
Vector of allocation ratios (default=1:2).
Vector ratio on control (default=1:2).
Shape of upper boundary. Either a function specifying the
shape or one of "pocock", "obf" (the default),
"triangular" and "fixed".
Shape of lower boundary. Either a function specifying the
shape or one of "pocock", "obf", "triangular" and
"fixed" (the default).
Fixed upper boundary (default=NULL). Only used if
shape="fixed".
Fixed lower boundary (default=0). Only used if
shape="fixed".
Starting point for finding the sample size
(default=1).
Stopping point for finding the sample size
(default=NULL).
Logical if sample size should be found as well
(default=TRUE).
Number of quadrature points per dimension in the outer integral
(default=20).
if TRUE (default), allows parallelisation of the
computation via a user-defined strategy specified by means of the function
future::plan(). If not set differently,
the default strategy is sequential, which corresponds to a
computation without parallelisation.
if TRUE (default), indicate at which stage the
computation is.
Vector of expected probabilities of falling into each category under control conditions. The elements must sum up to one (default=c(0.35, 0.4, 0.25)).
Interesting treatment effect on the scale of odds ratios (default=2).
Uninteresting treatment effect on the scale of odds ratios (default=1.2).
Number of experimental treatments (default=4).
Number of stages (default=2).
One-sided familywise error rate (default=0.05).
Desired power (default=0.9).
Vector of allocation ratios (default=1:2).
Vector ratio on control (default=1:2).
Shape of upper boundary. Either a function specifying the shape or one of "pocock", "obf" (the default), "triangular" and "fixed".
Shape of lower boundary. Either a function specifying the shape or one of "pocock", "obf", "triangular" and "fixed" (the default).
Fixed upper boundary (default=NULL). Only used if shape="fixed".
Fixed lower boundary (default=0). Only used if shape="fixed".
Starting point for finding the sample size (default=1).
Stopping point for finding the sample size (default=NULL).
Logical if sample size should be found as well (default=TRUE).
Number of quadrature points per dimension in the outer integral (default=20).
if TRUE (default), allows parallelisation of the computation via a user-defined strategy specified by means of the function future::plan(). If not set differently, the default strategy is sequential, which corresponds to a computation without parallelisation.
if TRUE (default), indicate at which stage the computation is.
Philip Pallmann
This function finds the (approximate) boundaries and sample size of a multi-arm multi-stage study with ordinal or binary endpoints with K active treatments plus control in which all promising treatments are continued at interim analyses as described in Magirr et al (2012). It is a wrapper around the basic mams function to facilitate its use with ordinal and binary endpoints, following ideas of Whitehead & Jaki (2009) and Jaki & Magirr (2013). For a binary endpoint the vector prob has only two elements (success/failure, yes/no, etc.). See mams for further details on the basic methodology.
Jaki T., Pallmann P. and Magirr D. (2019), The R Package MAMS for Designing Multi-Arm Multi-Stage Clinical Trials, Journal of Statistical Software, 88(4), 1-25. Link: doi:10.18637/jss.v088.i04
Magirr D., Jaki T. and Whitehead J. (2012), A generalized Dunnett test for multi-arm multi-stage clinical studies with treatment selection, Biometrika, 99(2), 494-501. Link: doi:10.1093/biomet/ass002
Magirr D., Stallard N. and Jaki T. (2014), Flexible sequential designs for multi-arm clinical trials, Statistics in Medicine, 33(19), 3269-3279. Link: doi:10.1002/sim.6183
Pocock S.J. (1977), Group sequential methods in the design and analysis of clinical trials, Biometrika, 64(2), 191-199.
O'Brien P.C., Fleming T.R. (1979), A multiple testing procedure for clinical trials, Biometrics, 35(3), 549-556.
Whitehead J. (1997), The Design and Analysis of Sequential Clinical Trials, Wiley: Chichester, UK.
# \donttest{
## An example based on the example in Whitehead & Jaki (2009)
# 2-stage design with triangular efficacy and futility boundaries
prob <- c(0.075, 0.182, 0.319, 0.243, 0.015, 0.166)
ordinal.mams(prob=prob, or=3.06, or0=1.32, K=3, J=2, alpha=0.05,
power=0.9, r=1:2, r0=1:2, ushape="triangular",
lshape="triangular")
# same example with parallelisation via separate R sessions running in the
# background
future::plan(multisession)
ordinal.mams(prob=prob, or=3.06, or0=1.32, K=3, J=2, alpha=0.05,
power=0.9, r=1:2, r0=1:2, ushape="triangular",
lshape="triangular", parallel=TRUE)
future::plan("default")
# }
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