Hi Michael, Another method to consider would be to fit the individuals who obviously were correctly sampled and then estimate the true infusion rate for the other individuals iteratively.
* Subset data to IDs with correct sampling * Fit model to those individuals * Subset points for incorrect sampling individuals to later times (time >> 1 hr) to estimate other parameters * Assume 1 hr total infusion and estimate Cmax/Tmax for these individuals (= actual end of infusion time) * Adjust data file so that time for incorrect sampling individuals is time + (Tmax - 1) * Fit model with new data file. Thanks, Bill -----Original Message----- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Leonid Gibiansky Sent: Thursday, June 02, 2011 12:45 PM To: mdkz...@aol.com Cc: nmusers@globomaxnm.com Subject: Re: [NMusers] Modeling concentration data with imprecise sampling time Michael, I do not think you can change observation time, but you can change infusion duration using RATE=-2 (in the data file) D1=THETA(*)*EXP(ETA()) (in the control stream) Alternative is to exclude data points that are inconsistent with the profiles. Leonid -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566 On 6/2/2011 11:44 AM, mdkz...@aol.com wrote: > > All: > I have a data set following a constant rate 1-hour infusion where the > time of 1-hour sample is imprecise. In some cases the "1-hr" sample was > taken prior to infusion termination as it was intended; however, in > others it was quite apparently take after the infusion ended. I'm > thinking of modeling with the time of the "1-hr" sample as a "theta". > Does anyone have a NONMEM coding example showing how to do this? Any > other suggestions as to how to handle this would be appreciated as well. > > Thanks, > Michael
