Re: Distribution of Simulated Cmax

From: Pravin Jadhav pravinj@gmail.com Subject: Re: [NMusers] Distribution of Simulated Cmax Date: Sat, 19 Nov 2005 12:55:27 -0500 Hi Partha, This is a classical simulation problem that we have on a few of occasions. You say, PPC did not show obvious flaws in the model. It would really depend on the metric that was used. Please take a look at our publication on the very same topic. Jadhav, P. R.; Gobburu, J.V.S.; A New Equivalence Based Metric for Predictive Check to Qualify Mixed-Effects Models, AAPS Journal, Vol. 7 No. 3 (2005) My initial guess is, the predictive check was done to assess average behavior of the data (Pp in our publication). Take a look at the equivalence based metric that was proposed. This metric would have been able to assess inconsistency between your data and the model (at Cmax). We think, this is one of the prime applications of predictive check, especially when used with the equivalence based metric. It will allow you to locate domains of interest where 'the model fails to reproduce the observed data'- underlying aim of the predictive check. You will need to take a look at the parameters that were used for simulation again. Look for any unusual values of CL, V, Ka etc./unusual combinations of those and then truncate the distributions accordingly (within the limits of the observed data). Hope it helps. Pravin Pravin Jadhav