Dear all - 

Cody and Bert have some amusing points. 

The problems with R that Cody states are no different than those that any 
organization has with any programming work.  Period.

We've mostly solved them through appropriate approaches, addressing 
through quality management, some of the issues raised by Cody with respect 
to 3-rd party packages, etc. 

Quality Management != Absolute Quality.

It's about risk management, as David's presentation of our work at UseR 
hopefully explained. 

It's about common sense. 

Combined, this can result in reasonable statements like (real names used 
in artificial examples to make a point):

        Martin M does X with R, I trust Martin, therefore I trust X done 
with R because the risk of wrong results in Y will have a low impact.

If Y happened to have an impact of $500m, then a reasonable approach might 
be to reconsider and find an additional expert, say Doug B, to confirm.

Alternatively, if you don't believe in expert opinions (or subjective 
probability, or mechanistic modeling), and feel like an empirical 
frequentist, you might just get a team of monkeys to verify that X in R 
seems correct, based on a project management strategy that incorporates 
someone's favorite IT risk mitigation approach.

With respect to Bert's points about 21CFR Part 11, please read the 
documents on the R WWW with respect to such things for a pretty informed 
opinion as to what is really happening.

I may not speak for Novartis, but it is possible that we'll be using a 
non-commercial version of R at some point in the future and we've been 
looking into the risk management strategies.   Some people are annoyed at 
the packages we will not let people use, but code review suggests that we 
really don't want people to use them (risk management, again).  The 
supporting infrastructure will be nice, but it'll also be a PITA to build.

But it really is just a matter of codification of common sense -- you 
should always put anything that you want to reproduce under version 
control, you should always have test cases to confirm that the 
implementations that you are using work in a few average cases (no one can 
cover every corner case) and you should make sure that you align your data 
and computer code with your reporting workflow.     It's the 
implementation of common sense that seems to be hard, as most R-help 
readers should be aware of by now.  I can't claim to implement it all the 
time, as readers of this list are probably additionally aware.

Best regards / Mit freundlichen Grüssen, 
Anthony (Tony) Rossini
Novartis Pharma AG
MODELING & SIMULATION
Group Head a.i., -- EU Statistical Modeling
CHBS, WSJ-027.1.012
Novartis Pharma AG
Lichtstrasse 35
CH-4056 Basel
Switzerland
Phone: +41 61 324 4186
Fax: +41 61 324 3039
Cell: +41 79 367 4557 (to send an SMS from Lotus Notes put the following: 
0041793674557@sms.novartis.com in the To box : -> only the content of the 
subject is sent)
Email : anthony.rossini@novartis.com

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