RE: Simultaneous PK and Cell Life Span PD modeling

Toufigh, When I first started this some very smart people told me that you should put an ETA on everything, because, biologically, there is variability on everything. Since, then, I've decided that this isn't a feasible approach. The same argument would be used to justify physiologically based pk models - even though they have two many parameters to be feasible. At the other end, if you talk to some very traditional statistician, they are entirely "data driven", don't put anything in that the data don't tell you to, i.e., ignore your knowledge of biology entirely. So, we all decide where we are on this empirical/theoretical spectrum. Personally, I like to think I'm somewhere in the middle - biologically based, but really need the data to tell me what should go into a model. The good news, I think, is that Bayesian statistics lets us do this. Could be done formally, but you'd have to specify, up front, how confident yo! u were that each feature is in the model. If you have lots of reason to believe that volume is a linear function of weight, then (IMHO) you are justified putting it in even if the current data in hand don't really tell you that. On the other hand, I'd be hesitant to put clearance as a function of astrological sign, even if the current data support that (unless the support is very, very strong) - because my prior is so low. People more knowledgeable about Bayesian methods could put this in a more formal structure. But, to your question, my answer is NO, you might start with a prior that there is an ETA on any given parameter, but if the current data don't support it (by which I mean either in terms of log-likelihood test [I know, you can't formally test whether and ETA is significant] or failure to converge) I don't hesitate to remove it. I don't see a reason to treat variance parameters any different from structural model parameters or co! variate parameters in this regard.Mark Mark Sale MD Next Level Solutions, LLC www.NextLevelSolns.com