change calculation of WRES?

Dear nmusers, I have a question which I hope is not too trivial for the group. I am currently analyzing some data where I have more trust in values at late time points. Therefore, I would like WRES to be a function of time, putting more weight on late time points. In the help guide I found that WRES can be influenced by SPTWO, however no real documentation exists (or I am not aware of it) about how to use it. Does anybody have an example for me of how to code this? Moreover, I noticed that no matter how I describe my weighting for IWRES, this does not at all seem to influence my objective function or parameter values. When evaluating a model, most people consider anyway that WRES is what counts, as IWRES are in most cases ok anyway. So my very simple question is: If this does not influence any of this and I don't use IWRES to decide if a model is good or bad, why bother at all to calculate them? I noticed that the standard errors seem to change depending on which weighting I use for IWRES. Can anybody explain this? Thanks and best wishes Nele ______________________________________________________________ Dr. Nele Kner Pharmacometrics -- Modeling and Simulation Nycomed GmbH Byk-Gulden-Str. 2 D-78467 Konstanz, Germany Fon: (+49) 7531 / 84 - 4759 Fax: (+49) 7531 / 84 - 94759 mailto: nele.kaessner http://www.nycomed.com County Court: Freiburg, Commercial Register HRB 701257 Chairman Supervisory Board: Charles Depasse Management Board: Dr. Barthold Piening, Gilbert Rademacher, Dr. Anders Ullman ---------------------------------------------------------------------- Proprietary or confidential information belonging to Nycomed Group may be contained in this message. If you are not the addressee indicated in this message, please do not copy or deliver this message to anyone. In such case, please destroy this message and notify the sender by reply e-mail. Please advise the sender immediately if you or your employer do not consent to Internet e-mail for messages of this kind. Opinions, conclusions and other information in this message that pertain to the sender's employer and its products and services represent the opinion of the sender and do not necessarily represent or reflect the views and opinions of the employer.

Dear nmusers, I have a question which I hope is not too trivial for the group. I am currently analyzing some data where I have more trust in values at late time points. Therefore, I would like WRES to be a function of time, putting more weight on late time points. In the help guide I found that WRES can be influenced by SPTWO, however no real documentation exists (or I am not aware of it) about how to use it. Does anybody have an example for me of how to code this? Moreover, I noticed that no matter how I describe my weighting for IWRES, this does not at all seem to influence my objective function or parameter values. When evaluating a model, most people consider anyway that WRES is what counts, as IWRES are in most cases ok anyway. So my very simple question is: If this does not influence any of this and I don't use IWRES to decide if a model is good or bad, why bother at all to calculate them? I noticed that the standard errors seem to change depending on which weighting I use for IWRES. Can anybody explain this? Thanks and best wishes Nele ______________________________________________________________ Dr. Nele Käßner Pharmacometrics -- Modeling and Simulation Nycomed GmbH Byk-Gulden-Str. 2 D-78467 Konstanz, Germany Fon: (+49) 7531 / 84 - 4759 Fax: (+49) 7531 / 84 - 94759 mailto: [email protected] http://www.nycomed.com County Court: Freiburg, Commercial Register HRB 701257 Chairman Supervisory Board: Charles Depasse Management Board: Dr. Barthold Piening, Gilbert Rademacher, Dr. Anders Ullman ---------------------------------------------------------------------- Proprietary or confidential information belonging to Nycomed Group may be contained in this message. If you are not the addressee indicated in this message, please do not copy or deliver this message to anyone. In such case, please destroy this message and notify the sender by reply e-mail. Please advise the sender immediately if you or your employer do not consent to Internet e-mail for messages of this kind. Opinions, conclusions and other information in this message that pertain to the sender's employer and its products and services represent the opinion of the sender and do not necessarily represent or reflect the views and opinions of the employer.

Dear Nele, Couldn't this be solved by simply turning on a flag after a certain time and estimate a seperate residual error when this flag is turned on? Ofcourse you can also code this flag in your dataset. Rob ________________________________

Dear Nele, Individual weighted residuals (IWRES) have nothing to do with the parameter estimation in NONMEM. IWRES are calculated and put in table files purely as a diagnostic variable. This way they can be helpful among other things to design a suitable RUV model (and that is what I really think you need to think about) . Normally I would not want to add weighting to different parts of the data based on my expectations. If I anticipated the case of lower residual unexplained variability (RUV) with time I would include such a feature in my RUV model and estimate parameters to describe it (and also evaluate it’s significance). The easiest check to do is to estimate one magnitude of residual error for early samples and one for late (see an example below for a simple additive RUV) . ;--------------------------------------------------------------------------- --------------------------------------------- IF(TIME.LT.X) THEN ; X = Time limit dividing early and late time points W = THETA(x) ; THETA (x) = standard deviation (SD) for RUV of early samples ELSE W = THETA(y) ; THETA (y) = SD for RUV of late samples ENDIF Y = IPRED+W*EPS(1) IRES = DV-IPRED IWRES = IRES/W $SIGMA 1 FIX ; Fixing SIGMA to variance 1 allows us to estimate the scaling factor W on standard deviation scale ;--------------------------------------------------------------------------- --------------------------------------------- I have no experience with a continuous RUV model that describes decreasing RUV with time. If the data is informative enough I sure that it is possible though. An example could look something like this: W0 = THETA(x) ; SD for W at time = 0 W1HL = THETA(y) ; Half-life of time dependent RUV WL = THETA(z) ; SD of non time dependent RUV W1K = LOG(2)/W1HL W1 = (W0- W2) * EXP(-W1K*TIME) W = W1 + WL Y = IPRED+W*EPS(1) $SIGMA 1 FIX ;--------------------------------------------------------------------------- --------------------------------------------- I hope this is of some help to you. Kind regards, Martin Bergstrand, MSc, PhD student ----------------------------------------------- Division of Pharmacokinetics and Drug Therapy, Department of Pharmaceutical Biosciences, Uppsala University ----------------------------------------------- P.O. Box 591 SE-751 24 Uppsala Sweden ----------------------------------------------- <mailto:[email protected]> [email protected] ----------------------------------------------- Work: +46 18 471 4639 Mobile: +46 709 994 396 Fax: +46 18 471 4003

Dear Nele I would normally be guided by the iWRES plots against individual predictions to determine if there is a time dependent trend that needs to be addressed in the error model (since the drug plasma concentrations vary with time). If so, you can use the following code in your error model: IF (TIME .LE. xxx) THEN W=yyy ELSE W=zzz ENDIF You can estimate the time i.e. put a THETA or put a fixed value yourself. The difference in the two values of W will tell you whether it is necessary to have a time varying error model. Further reading is available : Karlsson, Beal, Sheiner. 1995 "Three new residual error models for population PK/PD analyses" Journal of pharmacokinetics and biopharmaceutics, vol 23, no.6. Emmanuel Emmanuel Chigutsa (BPharm. Hons) Research Fellow, Pharmacometrics Group Division of Clinical Pharmacology, University of Cape Town K-45 Old Main Building, Groote Schuur Hospital Anzio Road, Observatory, 7925 Cape Town, South Africa Telephone: +27 214066758 Fax: +27 214066759 Mobile: +27 782826538 Email: [email protected] >>> <[email protected]> 25/08/2009 14:21 >>> Dear nmusers, I have a question which I hope is not too trivial for the group. I am currently analyzing some data where I have more trust in values at late time points. Therefore, I would like WRES to be a function of time, putting more weight on late time points. In the help guide I found that WRES can be influenced by SPTWO, however no real documentation exists (or I am not aware of it) about how to use it. Does anybody have an example for me of how to code this? Moreover, I noticed that no matter how I describe my weighting for IWRES, this does not at all seem to influence my objective function or parameter values. When evaluating a model, most people consider anyway that WRES is what counts, as IWRES are in most cases ok anyway. So my very simple question is: If this does not influence any of this and I don't use IWRES to decide if a model is good or bad, why bother at all to calculate them? I noticed that the standard errors seem to change depending on which weighting I use for IWRES. Can anybody explain this? Thanks and best wishes Nele ______________________________________________________________ Dr. Nele Käßner Pharmacometrics -- Modeling and Simulation Nycomed GmbH Byk-Gulden-Str. 2 D-78467 Konstanz, Germany Fon: (+49) 7531 / 84 - 4759 Fax: (+49) 7531 / 84 - 94759 mailto: [email protected] http://www.nycomed.com County Court: Freiburg, Commercial Register HRB 701257 Chairman Supervisory Board: Charles Depasse Management Board: Dr. Barthold Piening, Gilbert Rademacher, Dr. Anders Ullman ---------------------------------------------------------------------- Proprietary or confidential information belonging to Nycomed Group may be contained in this message. If you are not the addressee indicated in this message, please do not copy or deliver this message to anyone. In such case, please destroy this message and notify the sender by reply e-mail. Please advise the sender immediately if you or your employer do not consent to Internet e-mail for messages of this kind. Opinions, conclusions and other information in this message that pertain to the sender's employer and its products and services represent the opinion of the sender and do not necessarily represent or reflect the views and opinions of the employer. ---------------------------------------------------------------------- ______________________________________________________________________________________________ UNIVERSITY OF CAPE TOWN This e-mail is subject to the UCT ICT policies and e-mail disclaimer published on our website at http://www.uct.ac.za/about/policies/emaildisclaimer/ or obtainable from +27 21 650 4500. This e-mail is intended only for the person(s) to whom it is addressed. If the e-mail has reached you in error, please notify the author. If you are not the intended recipient of the e-mail you may not use, disclose, copy, redirect or print the content. If this e-mail is not related to the business of UCT it is sent by the sender in the sender's individual capacity. _____________________________________________________________________________________________________