Description Usage Arguments Details Value Note Author(s) References See Also Examples

`gsBoundCP()`

computes the total probability of crossing future upper bounds given an interim test statistic at an interim bound.
For each interim boundary, assumes an interim test statistic at the boundary and
computes the probability of crossing any of the later upper boundaries.

1 | ```
gsBoundCP(x, theta="thetahat", r=18)
``` |

`x` |
An object of type |

`theta` |
if |

`r` |
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 |

See Conditional power section of manual for further clarification. See also Muller and Schaffer (2001) for background theory.

A list containing two vectors, `CPlo`

and `CPhi`

.

`CPlo` |
A vector of length |

`CPhi` |
A vector of length |

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.

Muller, Hans-Helge and Schaffer, Helmut (2001), Adaptive group sequential designs for clinical trials:
combining the advantages of adaptive and classical group sequential approaches. *Biometrics*;57:886-891.

1 2 3 4 5 6 7 8 9 |

```
Loading required package: xtable
Loading required package: ggplot2
Asymmetric two-sided group sequential design with
90 % power and 2.5 % Type I Error.
Upper bound spending computations assume
trial continues if lower bound is crossed.
Sample
Size ----Lower bounds---- ----Upper bounds-----
Analysis Ratio* Z Nominal p Spend+ Z Nominal p Spend++
1 0.220 -0.90 0.1836 0.0077 3.25 0.0006 0.0006
2 0.441 -0.04 0.4853 0.0115 2.99 0.0014 0.0013
3 0.661 0.69 0.7563 0.0171 2.69 0.0036 0.0028
4 0.881 1.36 0.9131 0.0256 2.37 0.0088 0.0063
5 1.101 2.03 0.9786 0.0381 2.03 0.0214 0.0140
Total 0.1000 0.0250
+ lower bound beta spending (under H1):
Hwang-Shih-DeCani spending function with gamma = -2.
++ alpha spending:
Hwang-Shih-DeCani spending function with gamma = -4.
* Sample size ratio compared to fixed design with no interim
Boundary crossing probabilities and expected sample size
assume any cross stops the trial
Upper boundary (power or Type I Error)
Analysis
Theta 1 2 3 4 5 Total E{N}
0.0000 0.0006 0.0013 0.0028 0.0062 0.0117 0.0226 0.5726
3.2415 0.0417 0.1679 0.2806 0.2654 0.1444 0.9000 0.7440
Lower boundary (futility or Type II Error)
Analysis
Theta 1 2 3 4 5 Total
0.0000 0.1836 0.3201 0.2700 0.1477 0.0559 0.9774
3.2415 0.0077 0.0115 0.0171 0.0256 0.0381 0.1000
CPlo CPhi
[1,] 2.294534e-06 1.0000001
[2,] 2.238566e-03 0.9998352
[3,] 2.669114e-02 0.9922459
[4,] 1.296705e-01 0.9200502
CPlo CPhi
[1,] 0.4936972 0.9940265
[2,] 0.3676577 0.9954019
[3,] 0.3331896 0.9912361
[4,] 0.3871332 0.9590607
```

gsDesign documentation built on May 31, 2017, 2:15 a.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.