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)

Arguments

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 "pocock", "obf" (the default), "triangular" and "fixed".

lshape

Shape of lower boundary. Either a function specifying the shape or one of "pocock", "obf", "triangular" and "fixed" (the default).

ufix

Fixed upper boundary (default=NULL). Only used if shape="fixed".

lfix

Fixed lower boundary (default=0). Only used if shape="fixed".

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).

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, 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.

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")

[Package MAMS version 1.4.2 Index]