`gsBound()`

and `gsBound1()`

are lower-level functions used to find boundaries for a group sequential design.
They are not recommended (especially `gsBound1()`

) for casual users.
These functions do not adjust sample size as `gsDesign()`

does to ensure appropriate power for a design.

`gsBound()`

computes upper and lower bounds given boundary crossing probabilities assuming a mean of 0, the usual null hypothesis.
`gsBound1()`

computes the upper bound given a lower boundary, upper boundary crossing probabilities and an arbitrary mean (`theta`

).

1 2 |

Note that all vector arguments should have the same length which will be denoted here as `k`

.

`theta` |
Scalar containing mean (drift) per unit of statistical information. |

`I` |
Vector containing statistical information planned at each analysis. |

`a` |
Vector containing lower bound that is fixed for use in |

`trueneg` |
Vector of desired probabilities for crossing upper bound assuming mean of 0. |

`falsepos` |
Vector of desired probabilities for crossing lower bound assuming mean of 0. |

`probhi` |
Vector of desired probabilities for crossing upper bound assuming mean of theta. |

`tol` |
Tolerance for error (scalar; default is 0.000001). Normally this will not be changed by the user. This does not translate directly to number of digits of accuracy, so use extra decimal places. |

`r` |
Single integer value controlling grid for numerical integration as in Jennison and Turnbull (2000);
default is 18, range is 1 to 80.
Larger values provide larger number of grid points and greater accuracy.
Normally |

`printerr` |
If this scalar argument set to 1, this will print messages from underlying C program.
Mainly intended to notify user when an output solution does not match input specifications.
This is not intended to stop execution as this often occurs when deriving a design in |

The function `gsBound1()`

requires special attention to detail and knowledge of behavior when a design corresponding to the input parameters does not exist.

Both routines return a list. Common items returned by the two routines are:

`k` |
The length of vectors input; a scalar. |

`theta` |
As input in |

`I` |
As input. |

`a` |
For |

`b` |
The derived upper boundary required to yield the input boundary crossing probabilities under the null hypothesis. |

`tol` |
As input. |

`r` |
As input. |

`error` |
Error code. 0 if no error; greater than 0 otherwise. |

`gsBound()`

also returns the following items:

`rates` |
a list containing two items: |

`falsepos` |
vector of upper boundary crossing probabilities as input. |

`trueneg` |
vector of lower boundary crossing probabilities as input. |

`gsBound1()`

also returns the following items:

`problo` |
vector of lower boundary crossing probabilities; computed using input lower bound and derived upper bound. |

`probhi` |
vector of upper boundary crossing probabilities as input. |

The manual is not linked to this help file, but is available in library/gsdesign/doc/gsDesignManual.pdf in the directory where R is installed.

Keaven Anderson keaven\_anderson@merck.

Jennison C and Turnbull BW (2000), *Group Sequential Methods with Applications to Clinical Trials*.
Boca Raton: Chapman and Hall.

gsDesign package overview, `gsDesign`

, `gsProbability`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
# set boundaries so that probability is .01 of first crossing
# each upper boundary and .02 of crossing each lower boundary
# under the null hypothesis
x <- gsBound(I=c(1, 2, 3)/3, trueneg=array(.02, 3),
falsepos=array(.01, 3))
x
# use gsBound1 to set up boundary for a 1-sided test
x <- gsBound1(theta= 0, I=c(1, 2, 3) / 3, a=array(-20, 3),
probhi=c(.001, .009, .015))
x$b
# check boundary crossing probabilities with gsProbability
y <- gsProbability(k=3, theta=0, n.I=x$I, a=x$a, b=x$b)$upper$prob
# Note that gsBound1 only computes upper bound
# To get a lower bound under a parameter value theta:
# use minus the upper bound as a lower bound
# replace theta with -theta
# set probhi as desired lower boundary crossing probabilities
# Here we let set lower boundary crossing at 0.05 at each analysis
# assuming theta=2.2
y <- gsBound1(theta=-2.2, I=c(1, 2, 3)/3, a= -x$b,
probhi=array(.05, 3))
y$b
# Now use gsProbability to look at design
# Note that lower boundary crossing probabilities are as
# specified for theta=2.2, but for theta=0 the upper boundary
# crossing probabilities are smaller than originally specified
# above after first interim analysis
gsProbability(k=length(x$b), theta=c(0, 2.2), n.I=x$I, b=x$b, a= -y$b)
``` |

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