Replies to post #19744 on Biotech Values

[DewDiligence]

Re: Noise, philosophy, and DNDN

>Venturing into the philosophical, the only thing that makes "noise" noise is that it is unknown for the purposes of the whatever calculation you are making. For instance, communication devices, in this era of overlapping communications streams, often treat interference from neighbors as noise with characteristic X. Being treated as noise does NOT require that the thing be inherently unknowable - only that we choose to treat it as such.<

I had a strong feeling you would come back to me with a Qualcomm-inspired discussion :-)

In reply to your penultimate paragraph, I would submit that progress in the methods for evaluating clinical responses to new therapies has not kept up with the progress in medicine per se. Sometimes I wish I had pursued a career in this field. On the other hand, if I had done that, I would have missed out on the fun of biotech investing and reading biotech message boards :-)

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In regards to your final paragraph, walldiver raised an important point a few days ago: Cox can in some cases result in a p-value that is worse than the “raw” number. When that happens, companies are of course apt to tout the “raw” number as the one that really matters.

My contention is that Cox should be thought of like the fine-tuning knob on your old-fashioned radio. From a regulatory standpoint (as opposed to a theoretical one), it’s OK to run a pre-specified Cox analysis that improves the p-value modestly.

However, when Cox improves a p-value from 0.33 to 0.02, as it did in the case of DNDN’s 9902a trial, the data set is fatally flawed. I think this represents the biggest blind spot in the collective minds of the DNDN bulls. Regards, Dew

[Biowatch]

>>examples such as Iressa responders predicted via genotype) I would contend it should absolutely not make trials more expensive to run. That is running the wrong direction. More knowledge should make things cheaper and faster, not more expensive and slower. Ok, if you know for sure that one group will not respond then, yes, they should be excluded. But if you aren't sure, should you exclude them? Or have an analysis plan that allows some compensation. Such as Cox Regression (I suspect that there are better methods since Cox Regression is a little too much like data mining. Maybe prespecifying the HR and then backing out the covariates before calculating HR?).<<

Big pharma has gone back and forth with regards to analyzing the genetic profile of patients that would respond well or badly to a particular drug.

There are several factors in play.

1) They don't necessarily know what genetic profile to look for to determine who will benefit most (i.e., finding out which exact genetic profile benefits most patients for a given drug involves a whole new set of studies, and perhaps cutting edge science.)

2) They'd like to sell the drug to everyone to maximize sales, whether or not it will be the best drug for them.

3) If you could identify the 1% or 2% or 3% of patients who will have severe side effects in advance, and keep them from taking the drug in the first place, you can avoid having a drug pulled from the market.