Hi Leonid,

You're right.

Best regards,
Mats

Skickat från min iPhone

25 aug 2013 kl. 02:12 skrev "lgibian...@quantpharm.com" 
<lgibian...@quantpharm.com>:

> I think there was a typo, should it be: 
> IF(TIME.GT.OTIM) CCOV=OCOV+(T-OTIM)*(COV-OCOV)/(TIME-OTIM) ;CCOV is linear
> ?
> Leonid
> Original email:
> -----------------
> From: Mats Karlsson mats.karls...@farmbio.uu.se
> Date: Sat, 24 Aug 2013 10:02:00 +0200
> To: william.s.den...@pfizer.com, ellen.siwei...@gmail.com, 
> nmusers@globomaxnm.com
> Subject: RE: [NMusers] Time-varing covariate
> 
> 
> Dear Bill and Siwei,
> 
> 
> 
> Although the thought in Bill's reply is right, I think there is an error in
> the code. NONMEM by default uses the present values to update from the
> previous time. 
> 
> Further, it is possible to do this interpolation on the fly in the model
> file without changes to the data set. 
> 
> 
> 
> $INPUT ID TIME DV COV ;Cov is time-varying covariate
> 
> $PK
> 
> IF(NEWIND.NE.2) OTIM=0  ;initialize variable to store old time
> 
> IF(NEWIND.NE.2) OCOV=0  ;initialize variable to store old covariate value
> 
> 
> 
> $DES
> 
> CCOV=OCOV
> 
> IF(TIME.GT.OTIM) CCOV=OCOV+(T-TIME)*(COV-OCOV)/(TIME-OTIM) ;CCOV is linear
> interpolation between observed covariate values
> 
> 
> 
> $ERROR
> 
> OCOV =COV    ;store previous time 
> 
> OTIM  =TIME  ;store previous time
> 
> 
> 
> (NB haven't tested the code).
> 
> Best regards,
> 
> Mats
> 
> Mats Karlsson, PhD
> 
> Professor of Pharmacometrics
> 
> 
> 
> Dept of Pharmaceutical Biosciences
> 
> Faculty of Pharmacy
> 
> Uppsala University
> 
> Box 591
> 
> 75124 Uppsala
> 
> 
> 
> Phone: +46 18 4714105
> 
> Fax + 46 18 4714003
> 
> <http://www.farmbio.uu.se/research/researchgroups/pharmacometrics/>
> www.farmbio.uu.se/research/researchgroups/pharmacometrics/
> 
> 
> 
> From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On
> Behalf Of Denney, William S.
> Sent: 23 August 2013 20:09
> To: siwei Dai; nmusers@globomaxnm.com
> Subject: RE: [NMusers] Time-varing covariate
> 
> 
> 
> Hi Siwei,
> 
> 
> 
> If you are using an algebraic model (i.e. no differential equations), then
> you can simply include it in your equation:
> 
> 
> 
> e.g. assuming that SBP is systolic blood pressure in your original data set:
> 
> 
> 
> EFF=THETA(1)+SBP*THETA(2)
> 
> 
> 
> If you have a differential equation model and you want the time varying
> covariate to have an effect that is not a step change, you will need to
> interpolate the covariate.  Within your data set, you'll need a column for
> the next time and the next value of the time varying covariate.  Using the
> same assumption that you have SBP in your data set as your time varying
> covariate, you will want to make two new columns to allow for interpolation:
> 
> 
> 
> ID   TIME  NTIME  SBP  NSBP  DV
> 
> 1    0     1      110  115   5
> 
> 1    1     2      115  112   3
> 
> 1    2     4      112  108   4
> 
> 
> 
> Then to use your parameter, you will need code like the following in your
> $DES section to linearly interpolate:
> 
> 
> 
> $DES
> 
> .
> 
> ;; Current SBP
> 
> CSBP = (NSBP - SBP)/(NTIME - TIME) * (T - TIME) + SBP
> 
> .
> 
> 
> 
> The parameter T is the current time for the differential equation solver
> which will be somewhere between TIME and NTIME.  TIME is an important column
> name for NONMEM.
> 
> 
> 
> Thanks,
> 
> 
> 
> Bill
> 
> 
> 
> From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On
> Behalf Of siwei Dai
> Sent: Friday, August 23, 2013 12:01 PM
> To: nmusers@globomaxnm.com
> Subject: [NMusers] Time-varing covariate
> 
> 
> 
> Hi, Dear NMusers:
> 
> 
> 
> I want to add a time-varing covariate in my model. For example, blood
> pressure or blood flow as covariates. But I am not sure how to do it. I see
> some earlier threads to discuss it but they all use complicated methods.  
> 
> 
> 
> I am wondering if there are any new way  to do it in NM 7.2?  I see in the
> user guide that EVID=4 can indicate physiological change. Is this what I
> should use?
> 
> 
> 
> Thank you very much for any suggestions. 
> 
> 
> 
> Best regards,
> 
> 
> 
> Siwei
> 
> 
> 
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