>>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.
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