All, I think Leonid and Neil have pointed out two plausible explanations, I just wanted to highlight that these are two separate issues:
* If you have an additive component in your error model a graph on log scale would appear to widen at the end. This is fine. In this particular case since observations also widen at the end this may be a likely explanation, with the limited information nmusers have * If you have implemented a translation of proportional + additive on the log scale; this is only an approximation and in particular for simulations it may fall over. This occurs when IPRED is VERY close to zero. Typically this occurs around the time when drug is first absorbed (e.g. towards the end of lag time), but if you have rapid elimination I guess it can happen at the end of the time interval. This error model is not suitable when IPRED is very small, and then issue only appears during simulation. As a result, one may simulate odd observations with concentrations towards the infinite at the time of lag. If this is affecting you VPC you certainly need to deal with it and one solution would be to change your error model, as Neil suggests. As an alternative, putting a very low cutoff to the IPRED used in weighting the error would help. Since the cutoff is very low it will not affect estimation, but will remove unreasonable values during simulation. Best regards Jakob From: [email protected] [mailto:[email protected]] On Behalf Of Indranil Bhattacharya Sent: 10 May 2012 19:39 To: Toufigh Gordi Cc: [email protected] Subject: Re: [NMusers] VPC results using PsN and Xpose Hi Toufigh, I saw exactly the same scenario when using the proportional + additive model in the log domain. It probably has to do with the residual error as Leonid suggested. Converting the residual model to just additive and estimating the error as a THETA (by fixing SIGMA =1, W=THETA) removed the widening in the terminal part of the VPCs. Neil On Thu, May 10, 2012 at 12:49 PM, Toufigh Gordi <[email protected]<mailto:[email protected]>> wrote: Dear all, We have performed a VPC using PsN and the results were plotted using Xpose 4. An interesting feature of the graph is that the outer limits of the interval do not follow the typical curve smoothly but change with the observed data. As an example, toward the end of the time interval, where we have a larger variability in the observations, the lines widen and capture most of the data. I have difficulties understanding why the prediction lines behave this way. Any comments? Toufigh -- Indranil Bhattacharya
