On Thu, Feb 18, 2010 at 12:36 PM, Bert Gunter <gunter.ber...@gene.com> wrote: > The key dates are 1938 and 1962. The FDC act of 1938 essentially mandated > (demonstration of) safety. The tox testing infrastructure grew from that.At > that time, there were no computers, little data, little statistics > methodology. Statistics played little role -- as is still mainly the case > today for safety. Any safety findings whatever in safety testing raise a > flag; statistical significance in the multiple testing framework is > irrelevant.
> 1962 saw the Kefauver-Harris Amendments that mandated demonstration of > efficacy. That was the key. The whole clinical trial framework and the > relevant statistical design and analysis infrastructure flowed from that > regulatory requirement. SAS's development soon after was therefore the first > direct response to the statistical software needs that resulted. Note also, > that statistical software was in its infancy at this time: before SAS there > was Fortran and COBOL; there was no statistical software. > So, as you can see, there essentially was **no** "before SAS". > (Corrections/additional information welcome!) My recollection is that the BMD programs (which, in a later version, became BMDP) predated SAS and were specifically for BioMeDical analysis. Early statistical software was oriented to applications areas: SPSS (Statistical Package for the Social Sciences) was the predominant system used in the social sciences, BMD(P) in biomedical areas and SAS in agricultural/life sciences settings. Eventually the more coherent framework and comparative ease-of-use of SAS (yes, I am saying that with a straight face - in the days of batch jobs submitted on punched cards with data residing on magnetic tape, there were different standards of ease-of-use) won over more users in medical fields. > Bert Gunter > Genentech Nonclinical Biostatistics > > > > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On > Behalf Of Christopher W. Ryan > Sent: Thursday, February 18, 2010 10:09 AM > 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. > ______________________________________________ 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.