Replies to post #143570 on Biotech Values

[iwfal]

ARQL -

Then I want you to figure out what exactly was your mistake in applying your (correct) theories and methods to this particular case with ARQL. For instance, it could be that the way that you applied your theory and methods was wrong. It could be that you had the incorrect data. Or it could be that you made certain assumptions about the trial that were wrong. Or it could be that you made assumptions about the patient population that were wrong.

As a general rule it is often very difficult, even for the company, to figure out with any confidence where their predictions went wrong without running another trial. So it is even more difficult for an outsider to determine what went wrong - since they have access to much less data. It is part of what makes this investment sector so tough.

In fact, offhand I can think of only two cases where I am pretty sure I know what went wrong - in one instance they enrolled Eastern European patients in a semi-secretive fashion, and in another they clearly didn't target the right subgroup and when they did the post hoc analysis it was clear as day and they then went back and reran the ph iii successfully. In both cases I know what went wrong only because the company did thorough due diligence and then was open about the results. (I tend to have a bias towards tracking such analytic companies - and it has always worried me that it means I am like the drunk looking for change under the light. But I think that, in reality, the companies that are analytic in this fashion tend to be more successful.)

Soo, in the case of MARQUEE, ...if they release kinase markers vs various different subgroups then there is a reasonable probability it will be possible to determine where I (or they) went wrong. But, for example, they may not want to do that if they are successful and it shows some particular subgroups without much benefit.