tite.mams | R Documentation |
The function determines (approximately) the boundaries of a multi-arm multi-stage study with time-to-event endpoints for a given boundary shape and finds the required number of events.
tite.mams(hr=1.5, hr0=1.1, 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)
hr |
Interesting treatment effect on the scale of hazard ratios (default= |
hr0 |
Uninteresting treatment effect on the scale of hazard 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=20). |
parallel |
if |
print |
if |
This function finds the (approximate) boundaries and sample size of a multi-arm multi-stage study with time-to-event 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 time-to-event endpoints, following ideas of Jaki & Magirr (2013). Note that the sample size is calculated as the required number of events, from which the total sample size can be estimated (e.g., Whitehead 2001). 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, Dominic Magirr
Jaki T. and 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. Link: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.5669")}
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: \Sexpr[results=rd]{tools:::Rd_expr_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: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biomet/ass002")}
Whitehead J. (2001), Predicting the duration of sequential survival studies, Drug Information Journal, 35(4), 1387-1400.
print.MAMS
, summary.MAMS
, plot.MAMS
, mams
, MAMS
.
## An example 2-stage design with triangular efficacy and futility boundaries
tite.mams(hr=2, hr0=1.5, K=3, J=2, alpha=0.05, power=0.9,
r=1:2, r0=1:2, ushape="triangular", lshape="triangular")
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