Dendreon Corp fka DNDNQ

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The relevant issue is what we think the magnitude of this bias is. The bias could be 1% or it could be 91% depending on (in poster iwfal's example) among other parameters, the fraction of the underlying population of drugs that are truly effective (v. bogus ones), the rate of false IDs in these clinical trials, ....

This is correct. The problem with "program-survival bias" is that it is at too abstract a level to enable making any prediction for a particular instance with some measure of reliability. This thread of discussion really started with Dew's post below:

http://www.investorshub.com/boards/read_msg.asp?message_id=20615339

The problem that he framed was whether or not D9901+02a are good predictors of success for D9902b. The articulated sequence of reasoning steps was this: (1) asserting that the two phase-3 Provenge trials were like phase-2, (2) applying "program-survival bias" and (3) concluding that there is little predictive power from such phase-3's. (2) is a true statement but one must ask whether (1) was justified.

http://www.oncolink.org/conferences/article.cfm?c=3&s=26&ss=155&id=1082

The article that I quoted above, in fact, provides empirical data to guide in assessing such an assertion. These phase-3 trials were randomized and each of smallish size but together they were at a reasonable size. So, empirically they would be good predictors for similar conclusions in a new trial following the same protocol. So (1) above does not apply. Hence, (3) does not necessarily follow - except by chance.

Just to be clear, I am simply pointing out the fallacy in Dew's reasoning and not saying anything about the success probability of D9902b. There are better empirical ways to assess that probability based on simulation that Clark and others have done and I will do when I next have time to muck with my code.

I was a bit hard on Dew last night because of what he did to posters on the ivillage board, calling them delusional and sociopath. That post of his was thankfully deleted by ivillage management. But this also serves as a good example of sampling bias in doing statistics. If one was to sample Dew's posts solely from ivillage, one's impression of him would be rather uncharitable despite his obvious knowledge exhibited elsewhere.