R: Objects for handling with bi- and trifactorial trial data
carpetcube
R Documentation
Objects for handling with bi- and trifactorial trial data
Description
Create objects representing bi- or trifactorial clinical trial designs.
Usage
carpet(data,D,...)
cube(data,D,...)
Arguments
data
A list of numeric or binary data vectors from the
trial. See the details below for the order in which the list is to be given.
D
An integer vector of length 2 for carpet objects and
of length 3 for cube objects, specifying the number of doses of the components drugs in the trial.
...
Any further arguments.
Details
The function carpet creates objects of class carpet from the
specified data in the list that are used row-wise to fill up
the 2-factorial treatment groups, i.e. in the order
(0,0), (0,1),..., (0,D[2]), (1,0), ..., (1,D[2]), ..., (D[1],D[2]);
resulting in a (D[1]+1)x(D[2]+1) data array.
To represent trifactorial designs for the evaluation of a
three-compound combination, an object of class cube can be
created using the function cube. The data in the treatment
groups are then filled up in the order (0,0,0), ..., (0,0,D[3])
first, then (0,1,0), ..., (0,1,D[3]) and up to
(0,D[2],0), ..., (0,D[2],D[3]). This is the order also for
the values 0, ..., D[1] for the first component group, always
taking the data succesively from the list elements of data. The
result is a (D[1]+1)x(D[2]+1)x(D[3]+1) data array. Methods for
multiple inference and global tests can
be applied to carpet and cube objects.
Value
An object of class carpet or cube, respectively, with
the following slots.
data
The data list specified in the construction.
D
Vector of maximum doses specified in the construction.
n
Numeric vector of sample sizes in the respective groups.
Frommolt P, Hellmich M (2009): Resampling in multiple-dose factorial
designs. Biometrical J, 51(6), pp. 915-31
Hung HMJ, Chi GYH, Lipicky RJ (1993): Testing for the existence of a
desirable dose combination. Biometrics 49, pp. 85-94
Hung HMJ (2000): Evaluation of a combination drug with multiple doses
in unbalanced factorial design clinical trials. Statistics in
Medicine 19, pp. 2079-2087
See Also
bifactorial, mintest,
margint, avetest, maxtest
Examples
#Hypertension example from Hung (2000)
data(sidbp)
x<-split(sidbp$ynrmhom,sidbp$cb)
bifactorial<-carpet(data=x,D=c(2,3))
providing 
.