[R] Use of R in clinical trials

Bert Gunter gunter.berton at gene.com
Thu Feb 18 19:36:38 CET 2010

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

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!)

Bert Gunter
Genentech Nonclinical Biostatistics

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Christopher W. Ryan
Sent: Thursday, February 18, 2010 10:09 AM
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?

Christopher W. Ryan, MD
SUNY Upstate Medical University Clinical Campus at Binghamton
425 Robinson Street, Binghamton, NY  13904

"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
> 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
> pointed out, there are a host of factors other than ease of
> or even quality of results that weigh against any change. To reiterate,
> 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
> 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
> 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
> 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
> keep these issues in perspective. One's enthusiasm for R's manifold
> should not replace common sense and logic. That, too, would be
> 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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