[R] gsDesign

[Dongli Zhou]

Thank you so much for the help! It is really appreciated!

Dongli

On Nov 15, 2011, at 9:19 AM, Marc Schwartz <marc_schwartz at me.com> wrote:

> Hi Dongli,
> 
> Sorry for the delay in following up.
> 
> You might want to read the dsDesignManual.pdf document, which is available in the 'inst/doc' folder in the package source tarball on CRAN, or in the package 'doc' directory in your R installation. Use:
> 
>  system.file(package = "gsDesign")
> 
> to get the package top directory for your installation. The above file will be in the 'doc' sub-directory from there. It has more extensive worked examples than the default package manual.
> 
> 
> Simple non-inferiority example from ?nBinomial, with 2:1 ratio:
> 
> n.Fix <- nBinomial(p1 = .677, p2 = .677, delta0 = 0.07, ratio = 2)
> 
> 
>> n.Fix
> [1] 2056.671
> 
> # Adjust that *up* to an integer multiple of 3
> n.Fix <- 2058
> 
> 
> # Change 'outtype' to 2 if you want to see per arm sample sizes
> # eg:
>> nBinomial(p1 = .677, p2 = .677, delta0 = 0.07, ratio = 2, outtype = 2)
> $n1
> [1] 685.5569
> 
> $n2
> [1] 1371.114
> 
> 
> 
> 
> # Simple default GS design using the fixed study design sample size from above, 
> # which is not yet adjusted for interim analyses
> 
>> gsDesign(n.fix = n.Fix)
> 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.
> 
>                  ----Lower bounds----  ----Upper bounds-----
>  Analysis   N    Z   Nominal p Spend+  Z   Nominal p Spend++
>         1  734 -0.24    0.4057 0.0148 3.01    0.0013  0.0013
>         2 1468  0.94    0.8267 0.0289 2.55    0.0054  0.0049
>         3 2202  2.00    0.9772 0.0563 2.00    0.0228  0.0188
>     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
> 
> 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  Total   E{N}
>  0.0000 0.0013 0.0049 0.0171 0.0233 1286.0
>  0.0715 0.1412 0.4403 0.3185 0.9000 1628.4
> 
> Lower boundary (futility or Type II Error)
>          Analysis
>   Theta      1      2      3  Total
>  0.0000 0.4057 0.4290 0.1420 0.9767
>  0.0715 0.0148 0.0289 0.0563 0.1000
> 
> 
> So rather than needing 2058 from the fixed design, you actually need 2202 (1468 in one arm and 734 in the other).
> 
> I would urge you to read the manual I reference above and as Andy has noted in his reply, contact Keaven directly for further assistance with this package.
> 
> HTH,
> 
> Marc
> 
> On Nov 14, 2011, at 5:13 PM, Dongli Zhou wrote:
> 
>> Hi, Marc,
>> 
>> Thank you very much for the reply. I'm using the gsDesign function to create an object of type gsDesign. But the inputs do not include the 'ratio' argument.
>> 
>> Dongli 
>> 
>> On Nov 14, 2011, at 5:50 PM, Marc Schwartz <marc_schwartz at me.com> wrote:
>> 
>>> On Nov 14, 2011, at 4:11 PM, Dongli Zhou wrote:
>>> 
>>>> I'm trying to use gsDesign for a noninferiority trial with binary
>>>> endpoint. Did anyone know how to specify the trial with different sample
>>>> sizes for two treatment groups? Thanks in advance!
>>> 
>>> 
>>> Hi,
>>> 
>>> Presuming that you are using the nBinomial() function, see the 'ratio' argument, which defines the desired sample size ratio between the two groups.
>>> 
>>> See ?nBinomial and the examples there, which does include one using the 'ratio' argument.
>>> 
>>> HTH,
>>> 
>>> Marc Schwartz
>>> 
>