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" <cr...@binghamton.edu> >Sent: Feb 18, 2010 1:08 PM >To: r-help@r-project.org >Cc: p.dalga...@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 > >______________________________________________ >R-help@r-project.org mailing list >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
