[R] Use of R in clinical trials

[Kingsford Jones]

A John Chambers article (published back when S was but a twinkle in
his eye) provides an interesting snapshop of pre-SAS statistical
computing:

@article{chambers67,
  title={Some general aspects of statistical computing},
  author={Chambers, J.M.},
  journal={Journal of the Royal Statistical Society. Series C (Applied
Statistics)},
  volume={16},
  number={2},
  pages={124--132},
  year={1967},
  publisher={Blackwell Publishing; Royal Statistical Society}
}

BMD and P-stat are discussed in some detail, while the following get
mention in the reference section: TISER, BOMM, ASCOP, AARDVARK,
TARSIER, ZORILLA, GENSTAT, and STORM, among others.

best,

Kingsford Jones



On Thu, Feb 18, 2010 at 9:05 PM, <myrmail at earthlink.net> wrote:
>
> I am old enough to have lived through this particular transition.
> Prior to the advent of SAS, trials were analyzed by in-house written
> programs (usually in Fortran maybe with the help of IMSL). These
> programs were huge card decks. Having the card reader eat a card
> half way through reading the deck was a not unusual occurrence.
>
> I was responsible for deploying the first version of SAS. This meant
> compiling PL/I code stored on a magnetic tape and storing it on limited
> and expensive disk drives. It was several years before the transition
> from using in-house programs to SAS was completed. Yes there was a
> great deal of angst and I spent a lot of time convincing people that
> in the end there would be a cost advantage and overcoming institutional
> inertia.
>
> By the way, this was all done on computers that you will probably find
> only in a museum, if at all. These systems filled whole rooms and required
> a staff just to keep them running.
>
> Murray M Cooper, PhD
> Richland Statistics
> 9800 North 24th St
> Richland, MI  49083
>
>
>
> -----Original Message-----
> >From: "Christopher W. Ryan" <cryan at binghamton.edu>
> >Sent: Feb 18, 2010 1:08 PM
> >To: r-help at r-project.org
> >Cc: p.dalgaard at biostat.ku.dk
> >Subject: Re: [R] Use of R in clinical trials
> >
> >Pure Food and Drug Act: 1906
> >FDA: 1930s
> >founding of SAS: early 1970s
> >
> >(from the history websites of SAS and FDA)
> >
> >What did pharmaceutical companies use for data analysis before there was
> >SAS? And was there much angst over the change to SAS from whatever was
> >in use before?
> >
> >Or was there not such emphasis on and need for thorough data analysis
> >back then?
> >
> >--Chris
> >Christopher W. Ryan, MD
> >SUNY Upstate Medical University Clinical Campus at Binghamton
> >425 Robinson Street, Binghamton, NY  13904
> >cryanatbinghamtondotedu
> >
> >"If you want to build a ship, don't drum up the men to gather wood,
> >divide the work and give orders. Instead, teach them to yearn for the
> >vast and endless sea."  [Antoine de St. Exupery]
> >
> >Bert Gunter wrote:
> >> DISCLAIMER: This represents my personal view and in no way reflects that of
> >> my company.
> >>
> >> Warning: This is a long harangue that contains no useful information on R.
> >> May be wise to delete without reading.
> >> ----------
> >>
> >> Sorry folks, I still don't understand your comments. As Cody's original post
> >> pointed out, there are a host of factors other than ease of programmability
> >> or even quality of results that weigh against any change. To reiterate, all
> >> companies have a huge infrastructure of **validated SAS code** that would
> >> have to be replaced. This, in itself, would take years and cost tens of
> >> millions of dollars at least. Also to reiterate, it's not only
> >> statistical/reporting functionality but even more the integration into the
> >> existing clinical database systems that would have to be rewritten **and
> >> validated**. All this would have to be done while continuing full steam on
> >> existing submissions. It is therefore not surprising to me that no pharma
> >> company in its right mind even contemplates undertaking such an effort.
> >>
> >> To put these things into perspective. Let's say Pfizer has 200 SAS
> >> programmers (it's probably more, as they are a large Pharma, but I dunno).
> >> If each programmer costs, conservatively, $200K U.S. per year fully loaded,
> >> that's $40 million U.S. for SAS Programmers. And this is probably a severe
> >> underestimate. So the $14M quoted below is chicken feed -- it doesn't even
> >> make the radar.
> >>
> >> To add further perspective, a single (large) pivotal clinical trial can
> >> easily cost $250M . A delay in approval due to fooling around trying to
> >> shift to a whole new software system could easily cause hundreds of million
> >> to billions if it means a competitor gets to the marketplace first. So, to
> >> repeat, SAS costs are chicken feed.
> >>
> >> Yes, I suppose that the present system institutionalizes mediocrity. How
> >> could it be otherwise in any such large scale enterprise? Continuity,
> >> reliability, and robustness are all orders of magnitude more important for
> >> both the FDA and Pharma to get safe and efficacious drugs to the public.
> >> Constantly hopping onto the latest and greatest "craze" (yes, I exaggerate
> >> here!) would be dangerous, unacceptable, and would probably delay drug
> >> approvals. I consider this another example of the Kuhnsian paradigm (Thomas
> >> Kuhn: "The Structure of Scientific Revolutions")in action.
> >>
> >> This is **not** to say that there is not a useful role for R (or STATA or
> >> ...) to play in clinical trial submissions or, more generally, in drug
> >> research and development. There certainly is. For the record, I use R
> >> exclusively in my (nonclinical statistics) work. Nor is to say that all
> >> change must be avoided. That would be downright dangerous. But let's please
> >> keep these issues in perspective. One's enthusiasm for R's manifold virtues
> >> should not replace common sense and logic. That, too, would be unfortunate.
> >>
> >> Since I've freely blustered, I am now a fair target. So I welcome forceful
> >> rebuttals and criticisms and, as I've said what I wanted to, I will not
> >> respond. You have the last word.
> >>
> >> Bert Gunter
> >> Genentech Nonclinical Biostatistics
> >
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