Re: RE: Proper way to handle the pre-first dose PK observation for non-endogenous drug

On 8/11/2012 12:35 p.m., Tatosian, Daniel wrote: > Hi Nick, > > I disagree with the suggestion to use exact zero for predose. Residual error > models are, for a perfect structural model, a model for assay uncertainty. By > including many observations at predose as precise zero, you are modeling non > assay observations with an assay model. Result is your estimate of assay > variance is biased below actual assay noise, just as observed by Yamings test. > > Yaming you can either exclude, or if you like you could try the typical > censored data approaches. But i usually exclude predose myself as it doesn't > inform the model. > > Best regards > Dan > > Sent from my iPhone > > On Nov 7, 2012, at 6:19 PM, "Nick Holford" <[email protected]> wrote: > > > Yaming, Matt, > > > > I would do exactly what Yaming has done already. Treat the pre-dose > > measurements as true observations for when the predicted conc is zero. > > > > It is not true to say they provide no information about model > > parameters. They are the best way to improve your estimate of the > > additive error parameter (independent of PK model misspecification). By > > improving the residual error model you may also have benefits in > > improving your PK model. Although the PK model benefit may be small in > > principle it is foolish to ignore data that could be helpful. > > > > A major weakness of using log transformed both sides approach is that it > > cannot use these real observations which is why I have rarely used it. > > > > Best wishes, > > > > Nick > > > > On 8/11/2012 11:23 a.m., Fidler,Matt,FORT WORTH,R&D wrote: > > > > > Yaming, > > > > > > As you pointed-out DV=Prediction. Including these data-points biases > > > your estimate of the additive component of variability. My opinion is > > > just to exclude the observations to get a better estimate of additive > > > variability. > > > > > > On a side note Additive+Proportortional is similar to a > > > lognormal-error structure. In a lognormal error structure zero > > > observations have to be excluded anyway. > > > > > > Matt. > > > > > > *From:*[email protected] > > > [mailto:[email protected]] *On Behalf Of *Yaming Hang > > > *Sent:* Wednesday, November 07, 2012 4:04 PM > > > *To:* [email protected] > > > *Subject:* [NMusers] Proper way to handle the pre-first dose PK > > > observation for non-endogenous drug > > > > > > Dear NONMEM Users, > > > > > > I’d like to get some advice from you with regard to how to handle the > > > pre-first dose PK observation when the drug is not an endogenous > > > substance. > > > > > > I tried too different approaches, one approach is treating them as > > > missing values (DV=0, EVID=0, MDV=1), another is treating them as true > > > 0s (DV=0, EVID=0, MDV=0). My error structure is proportional + > > > additive. There were very little difference for all parameters except > > > for the SD of the additive error. When these pre-first dose > > > concentrations were treated as missing, the estimated omega for > > > additive error is 3.92, and when they were treated as true 0s, the > > > sigma became 2.85. > > > > > > To me, in theory, these values provide no information about the model > > > parameters because the system will predict them to be 0 at time 0 > > > anyway for any point in the parameter space. Is what happened here > > > that because DV is exactly the same as prediction, therefore the > > > estimation of additive residual error variance has been brought down? > > > > > > Which way is more appropriate? I’d really appreciate it if you can > > > share your experience/insight. > > > > > > Yaming Hang, Ph.D. > > > > > > Pharmacometrics > > > > > > Biogen Idec > > > > > > 14 Cambridge Center > > > > > > Cambridge, MA 02142 > > > > > > Office: 781-464-1741 > > > > > > Fax: 617-679-2804 > > > > > > Email: [email protected] <mailto:[email protected]> > > > > > > ------------------------------------------------------------------------ > > > This e-mail (including any attachments) is confidential and may be > > > legally privileged. If you are not an intended recipient or an > > > authorized representative of an intended recipient, you are prohibited > > > from using, copying or distributing the information in this e-mail or > > > its attachments. If you have received this e-mail in error, please > > > notify the sender immediately by return e-mail and delete all copies > > > of this message and any attachments. > > > > > > Thank you. > > > > -- > > Nick Holford, Professor Clinical Pharmacology > > Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A > > University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand > > tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53 > > email: [email protected] > > http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford > > Notice: This e-mail message, together with any attachments, contains > information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, > New Jersey, USA 08889), and/or its affiliates Direct contact information > for affiliates is available at > http://www.merck.com/contact/contacts.html) that may be confidential, > proprietary copyrighted and/or legally privileged. It is intended solely > for the use of the individual or entity named on this message. If you are > not the intended recipient, and have received this message in error, > please notify us immediately by reply e-mail and then delete it from > your system. -- Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53 email: [email protected] http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford