bbcdRand: Sampling algorithm for bbcd
In randomizeR: Randomization for Clinical Trials
bbcdRand R Documentation
Sampling algorithm for bbcd
Description
Sampling algorithm for bbcd
Usage
bbcdRand(N, a, K = 2)
Arguments
N
integer for the total sample size of the trial.
a
nonnegative parameter which controls the degree of randomness:
For decreasing a the allocations become deterministic, while for increasing
a the randomization procedure tends to complete randomization.
K
number of treatment groups (e.g. K=2 if we compare one experimental
against one control treatment).
Value
A vector with the allocation sequence for a clinical trial.
It will contain a zero (resp. 1) at position i, when patient i
is allocated to treatment A (resp. B).
References
A. B. Antognini and Maroussa Zagoraiou (2014) Balance and randomness in sequential
clinical trials: the dominant biased coin design.
Pharmaceutical Statistics
13(2), 119-127
randomizeR documentation built on Sept. 19, 2023, 1:08 a.m.
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| bbcdRand | R Documentation |
Sampling algorithm for bbcd
Description
Sampling algorithm for bbcd
Usage
bbcdRand(N, a, K = 2)
Arguments
N |
integer for the total sample size of the trial. |
a |
nonnegative parameter which controls the degree of randomness:
For decreasing |
K |
number of treatment groups (e.g. K=2 if we compare one experimental against one control treatment). |
Value
A vector with the allocation sequence for a clinical trial.
It will contain a zero (resp. 1) at position i, when patient i
is allocated to treatment A (resp. B).
References
A. B. Antognini and Maroussa Zagoraiou (2014) Balance and randomness in sequential clinical trials: the dominant biased coin design. Pharmaceutical Statistics 13(2), 119-127
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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