ordinal.mams {MAMS} | R Documentation |
Function to design multi-arm multi-stage studies with ordinal or binary endpoints
Description
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.
Usage
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, N=20,
parallel=TRUE, print=TRUE)
Arguments
prob |
Vector of expected probabilities of falling into each category under control conditions. The elements must sum up to one (default= |
or |
Interesting treatment effect on the scale of odds ratios (default= |
or0 |
Uninteresting treatment effect on the scale of odds ratios (default= |
K |
Number of experimental treatments (default= |
J |
Number of stages (default= |
alpha |
One-sided familywise error rate (default= |
power |
Desired power (default= |
r |
Vector of allocation ratios (default= |
r0 |
Vector ratio on control (default= |
ushape |
Shape of upper boundary. Either a function specifying the shape or one of |
lshape |
Shape of lower boundary. Either a function specifying the shape or one of |
ufix |
Fixed upper boundary (default= |
lfix |
Fixed lower boundary (default= |
nstart |
Starting point for finding the sample size (default= |
nstop |
Stopping point for finding the sample size (default= |
sample.size |
Logical if sample size should be found as well (default= |
N |
Number of quadrature points per dimension in the outer integral (default= |
parallel |
if |
print |
if |
Details
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.
Value
An object of the class MAMS containing the following components:
l |
Lower boundary. |
u |
Upper boundary. |
n |
Sample size on control in stage 1. |
N |
Maximum total sample size. |
K |
Number of experimental treatments. |
J |
Number of stages in the trial. |
alpha |
Familywise error rate. |
alpha.star |
Cumulative familywise error rate spent by each analysis. |
power |
Power under least favorable configuration. |
rMat |
Matrix of allocation ratios. First row corresponds to control while subsequent rows are for the experimental treatments. |
Author(s)
Philip Pallmann
References
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.
See Also
print.MAMS
, summary.MAMS
, plot.MAMS
, mams
, MAMS
.
Examples
## 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")