ordinal.mams {MAMS} | R Documentation |
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, N=20)
prob |
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)). |
or |
Interesting treatment effect on the scale of odds ratios (default=2). |
or0 |
Uninteresting treatment effect on the scale of odds ratios (default=1.2). |
K |
Number of experimental treatments (default=4). |
J |
Number of stages (default=2). |
alpha |
One-sided familywise error rate (default=0.05). |
power |
Desired power (default=0.9). |
r |
Vector of allocation ratios (default=1:2). |
r0 |
Vector ratio on control (default=1:2). |
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=NULL). Only used if |
lfix |
Fixed lower boundary (default=0). Only used if |
nstart |
Starting point for finding the sample size (default=1). |
nstop |
Stopping point for finding the sample size (default=NULL). |
sample.size |
Logical if sample size should be found as well (default=TRUE). |
N |
Number of quadrature points per dimension in the outer integral (default=20). |
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.
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. |
Philip Pallmann
Jaki T, Magirr D (2013) Considerations on covariates and endpoints in multi-arm multi-stage clinical trials selecting all promising treatments. Statistics in Medicine, 32(7), 1150-1163.
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), pp. 1-25. doi: 10.18637/jss.v088.i04 (URL: http://doi.org/10.18637/jss.v088.i04)
Magirr D, Stallard N, Jaki T. (2014) Flexible sequential designs for multi-arm clinical trials. Statistics in Medicine. 33(19):3269-3279.
Magirr D, Jaki T, Whitehead J (2012) A generalized Dunnett test for multi-arm multi-stage clinical studies with treatment selection. Biometrika, 99(2), 494-501.
Whitehead J, Jaki T (2009) One- and two-stage design proposals for a phase II trial comparing three active treatments with control using an ordered categorical endpoint. Statistics in Medicine, 28(5), 828-847.
## 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")