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

-----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Fidler,Matt,FORT WORTH,R&D Sent: Thursday, November 08, 2012 9:29 AM To: Nick Holford; nmusers Subject: RE: [NMusers] RE: Proper way to handle the pre-first dose PK observation for non-endogenous drug All, I agree. As a side note, if you have the actual BLQ observations that are above zero, then a lognormal error model can be used. No observations will be excluded. Matt. -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Nick Holford Sent: Wednesday, November 07, 2012 5:50 PM To: nmusers Subject: Re: [NMusers] RE: Proper way to handle the pre-first dose PK observation for non-endogenous drug Daniel Sorry -- thanks for picking that up! It seems I didn't read what Yaming wrote properly. Perhaps because I couldn't imagine why anyone would replace a real measurement with zero! Beal pointed out the folly of replacing BLQ values with zero in 2001. Notice that I said to use the pre-dose MEASUREMENTS --- not imputed values of zero. What I wanted to recommend was using the measured pre-dose value and including that in the data. Its important not to censor those measured values and include negative measurements as well as positive (or even zero) measurements. With a true additive error model and true prediction of zero there can be both negative and positive measurements. Of course if the chemical analysts refuse to tell the truth and report this as BLQ then you may resort to using a missing data method such as that proposed by Beal (e.g. M3 or M4). Nick Beal SL. Ways to fit a PK model with some data below the quantification limit. Journal of Pharmacokinetics & Pharmacodynamics 2001; 28: 481-504. 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 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.