VPC results using PsN and Xpose

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

Toufigh It would help to see the plot. One possibility is that the plot is in the log scale, and you have additive error component that plays larger role at low concentrations. Leonid -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566

Or, maybe it's bin problem. Kun Wang '[email protected]'<[email protected]>; -----Original Message----- From: Leonid Gibiansky Sent: Thursday, May 10, 2012 10:58 AM To: Toufigh Gordi Cc: ' [email protected] ' Subject: Re: [NMusers] VPC results using PsN and Xpose Toufigh It would help to see the plot. One possibility is that the plot is in the log scale, and you have additive error component that plays larger role at low concentrations. Leonid -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566

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

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