[R] SPLUS Seqtrial vs. R Packages for sequential clinical trials designs

Marc Schwartz marc_schwartz at me.com
Thu Dec 17 20:13:39 CET 2009

On Dec 17, 2009, at 9:58 AM, Paul Miller wrote:

> Hello Everyone,
> I’m a SAS user who has recently become interested in sequential  
> clinical trials designs. I’ve discovered that the SAS based  
> approaches for these designs are either too costly or are  
> “experimental.” So now I’m looking for alternative software. Two  
> programs that seem promising are SPLUS Seqtrial and R.
> I recently obtained a 30 day trial for the SPLUS Seqtrial add-on and  
> have worked my way through most of the examples in the manual. I’ve  
> also gotten access to R, installed a package called gsDesign, and  
> worked through most of the examples in its documentation.
> Although I don’t yet have a good understanding of the various  
> approaches to sequential clinical trials designs, I thought that the  
> gsDesign package seemed very impressive. I also understand that  
> there are several other R packages that relate to sequential  
> clinical trials designs, such as AGSDest , GroupSeq, ldbounds,  
> MChtest, PwrGSD, and Seqmon. Some of these seem fairly comprehensive  
> while others seem to focus on a single approach.
> My questions center on the adequacy of SPLUS Seqtrial and the R  
> Packages. I was wondering if there is anyone out there who would be  
> familiar enough with these to comment on their relative merits. Will  
> SPLUS Seqtrial or the R packages allow me to do all the designs I’m  
> ever likely to need? If I pay for SPLUS Seqtrial, will I get  
> anything that I can’t get using the various R packages? Are any of  
> the R packages comprehensive? Or would it at least be possible to  
> cover all the types of designs that are commonly used by employing a  
> variety of R packages? What kind of validation work generally goes  
> into an R package and how would this likely compare to the sort of  
> validation work that has gone into Seqtrial?
> There may be other questions that I should be asking but haven’t  
> thought of.
> At any rate, if some of you would be willing to share some advice or  
> insights I’d greatly appreciate it.
> Thanks,
> Paul

In addition to David's pointer to the relevant CRAN Task View, I would  
point you to the following document, which is applicable to R ("Base  
R" and "Recommended Packages" only):


It does not however, apply to user contributed packages (on CRAN or  

When it comes to validation, the majority of the burden is on you and  
not on the vendor. The vendor can document their own SDLC and the  
document above covers R's, within the regulatory domain of clinical  
trials (eg. 21 CFR 11, etc.). However, you cannot validate software in  
a vacuum, so you must implement that process as appropriate for your  
own shop.

For user contributed packages, you will want to consider communicating  
with the package authors/maintainers on their SDLC. Some will no doubt  
be better than others and only you can make the risk/benefit  
assessment along with your implementing the requisite internal IQ/OQ/ 
PQ procedures.

 From purely a functional comparison perspective, you would need to  
list the functionality of the S+ SeqTrial product and then compare it  
with the functionality available in R and the user contributed  
packages. Only you can make the 'value judgement' as to cost versus  

It is impossible to project into the future to know what potential  
trial designs you may encounter in your work. It is also impossible to  
know what new techniques (especially for adaptive designs) may become  
available moving forward.

If you search the list archives, using keywords such as SAS or FDA,  
you will find a plethora of discussions on the general topics of R  
versus SAS, etc. You will also see many discussions on R and clinical  
trials, including many of the misconceptions regarding validation and  
the FDA (which BTW, uses R internally).

As a SAS user, on a more general level, you may find the following  
book to be helpful in moving to R:

   R for SAS and SPSS Users
   Robert A. Muenchen


Marc Schwartz

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