stepdown.mams {MAMS} | R Documentation |
The function determines stopping boundaries for all intersection hypothesis tests in a multi-arm multi-stage study, given the amount of alpha (familywise error rate) to be spent at each analysis.
stepdown.mams(nMat=matrix(c(10, 20), nrow=2, ncol=4), alpha.star=c(0.01, 0.025), lb=0, selection="all.promising")
nMat |
Matrix containing the cumulative sample sizes in each treatment arm (columns: control, trt 1, ..., trt K), at each analysis (rows). The number of analyses must be either 2 or 3 (default=matrix(c(10, 20), nrow=2, ncol=4)). |
alpha.star |
Cumulative familywise error rate to be spent at each analysis (default=c(0.01, 0.025)). |
lb |
Fixed lower boundary (default=0). |
selection |
How are treatments selected for the next stage? Using the default "all.promising" method, all treatments with a test statistic exceeding the lower boundary are taken forward to the next stage. If "select.best", only the treatment with the largest statistic may be selected for future stages. (default="all.promising"). |
The function implements the methods described in Magirr et al. (2014) to find individual boundaries for all intersection hypotheses.
An object of the class MAMS.stepdown containing the following components:
l |
Lower boundaries. |
u |
Upper boundaries. |
nMat |
Cumulative sample sizes on each treatment arm. |
K |
Number of experimental treatments. |
J |
Number of stages in the trial. |
alpha.star |
Cumulative familywise error rate spent at each analysis. |
selection |
Pre-specified method of treatment selection. |
zscores |
A list containing the observed test statistics at analyses so far (at the design stage this is NULL). |
selected.trts |
A list containing the treatments selected for each stage. |
Dominic Magirr
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, Jaki T, Whitehead J (2012) A generalized Dunnett test for multi-arm multi-stage clinical studies with treatment selection. Biometrika, 99(2), 494-501.
Magirr D, Stallard N, Jaki T (2014) Flexible sequential designs for multi-arm clinical trials. Statistics in Medicine, 33(19), 3269-3279.
Stallard N, Todd S (2003) Sequential designs for phase III clinical trials incorporating treatment selection. Statistics in Medicine, 22(5), 689-703.
# Note that some of these examples may take a few minutes to run # 2-stage 3-treatments versus control design, all promising treatments are selected: stepdown.mams(nMat=matrix(c(10, 20), nrow=2, ncol=4), alpha.star=c(0.01, 0.05), lb=0, selection="all.promising") # select the best treatment after the first stage: stepdown.mams(nMat=matrix(c(10, 20), nrow=2, ncol=4), alpha.star=c(0.01, 0.05), lb=0, selection="select.best") # 3 stages and unequal randomization: stepdown.mams(nMat=matrix(c(20, 40, 60, rep(c(10, 20, 30), 3)), nrow=3, ncol=4), alpha.star=c(0.01, 0.025, 0.05), lb=c(0, 0.75), selection="all.promising")