Dear Luann,
(1) My data file was recorded with covariates values changed each
times in according to the lab monitored. Hence, I think I have time-vary
covariates in datafile. Now, I use normal control stream to model these
data. So, you suggested me to put a new value on a record with matching
date and then retain forward to the next covariate sample. From this
suggestion, let me confirm that I should have one column for baseline
covariate (1st Lab monitoring) and another column for exact covariates
recorded on that day?
(2) Then, how could I code the control stream for the covariate
model with time-varying covarites? (sorry that I have never get into it)
(3) Btw, I have one doubtful question about stationary covariates
on the data file. Is it possible to model the PPKs of the drugs with
stationary covariates. I mean that is it rationale to use only
one value of each covariates in the model wheres the concentration+dose
were dynamic especially if the study period take quite long time.
Thank you so much for your reply, valued comments and
suggestions.
Kind regards,
Vichapat
On Tue, Jul 9, 2019 at 8:58 PM Luann Phillips <lu...@cognigencorp.com>
wrote:
> Vichapat,
>
> Your ctl stream appears to be correct. To model with time-varying
> covariates involves a change in the database.
> (A) Did you use the covariate values at the time of each patient's first
> dose (ie, baseline values) in the data?
> or
> (B) Did you use the covariate values each time that they were collected?
>
> (A) is stationary covariates and (B) is time-vary covariates.
>
> To include time-varying covariates in the data, put the new value on a
> record with a matching date and then retain forward to the next covariate
> sample.
>
> Please be aware that the dosing and sample time assumptions (which
> sometimes are required) will also add to unexplained variability. I would
> look at plot of the data prior to running any models and exclude any
> concentrations that look very wrong (ie, collected at a peak instead of a
> trough). Perform the modeling and then try re-including the 'wrong'
> concentrations to show the impact to the model but I would still make the
> final model the one excluding those concentrations.
>
> Luann
>
> ------------------------------
> *From: *"Vichapat Tharanon" <vichapa...@gmail.com>
> *To: *"Luann Phillips" <luann.phill...@cognigencorp.com>
> *Sent: *Monday, July 8, 2019 10:06:06 PM
> *Subject: *Re: [NMusers] Is it possible that IIV (%CV) of final model was
> higher than IIV of base model?
>
> Dear Luann,
>
> Thank you so much for your valued suggestions. I greatly
> appreciated it. By the way, The suggestion given me that mean I should use
> "Time varying covariates" on the model? I am really new with NONMEM, If
> you do not mind helping me. Could you suggest me how to code the control
> file for that model in right way. I really know that my request may disturb
> you, but I do not know how to start it. Thank you in advance.
>
> Best regards,
>
> PS, This is my original control file for final model. There are 1170
> Tacrolimus concentration from 50 patients (retrospective data) then I
> assumed all patient took a drug at same time (every 12 hours: AM, PM on
> time) and Trough concentrations were monitored at 11.50 hours (Before the
> next morning dose 30 minutes).
> Briefly, tacrolimus was reported high inter- & intra-variability and
> primarily metabolized by liver via Cytochrome enzyme and eliminated via
> bile.
>
> ;Model Desc: Final model
> ;Project Name: step3cov
> ;Project ID: NO PROJECT DESCRIPTION
> ;Project ID: NO PROJECT DESCRIPTION
>
> $PROB RUN# ALTHGBTB
> $INPUT C ID TIME AMT ADDL II TAD DV MDV EVID BW POD AST ALT ALP GGT TB DB
> ALB HGB HCT BUN SCR
> $DATA MASTER.CSV IGNORE=C
> $SUBROUTINES ADVAN2 TRANS2
> $PK
>
>
> TVCL=THETA(1)*EXP(THETA(4)*(ALT/388))*((HGB/10.50)**THETA(5))*((TB/4.7)**THETA(6))
> CL=TVCL*EXP(ETA(1))
> TVV=THETA(2)
> V=TVV*EXP(ETA(2))
> TVKA=THETA(3)
> KA=TVKA*EXP(ETA(3))
> S2=V/1000
>
> $ERROR
> IPRE=F
> W= 1
> IRES= DV-IPRE
> IWRE=(DV-IPRE)/W
> Y = F + ERR(1)
>
> $EST METHOD=1 INTERACTION PRINT=5 MAX=9999 SIG=3 MSFO=ALTHGBTB.msf
> $THETA
> (0,20) ;[CL/F]
> (0,500) ;[V/F]
> (fixed,4.48) ;[KA]
> (0.001);[ALT]
> (0.001);[HGB]
> (0.001);[TB]
>
> $OMEGA
> 0.04 ;[P] omega(1,1)
> 0.04 ;[P] omega(2,2)
> (fixed,0) ;[A] omega(3,3)
> $SIGMA
> 0.04 ;[A] sigma(1,1)
>
> $COV
> $TABLE ID CL V KA ETA1 ETA2 ETA3 PRED RES WRES IPRE IWRE CPRED CWRES TIME
> AMT ADDL II TAD DV BW POD AST ALT ALP GGT TB DB ALB HGB HCT BUN SCR TIME
> ONEHEADER NOPRINT FILE=ALTHGBTB.tab
> $TABLE ID TIME CL V KA ETA1 ETA2 ETA3 ONEHEADER NOPRINT FILE=PATABALTHGBTB
> $TABLE ID BW POD AST ALT ALP GGT TB DB ALB HGB HCT BUN SCR ONEHEADER
> NOPRINT FILE=COTABALTHGBTB
> $TABLE ID ONEHEADER NOPRINT FILE=CATABALTHGBTB
> $TABLE ID TIME PRED RES WRES IPRE IWRE CPRED CWRES ONEHEADER NOPRINT
> FILE=SDTABALTHGBTB
> $TABLE ID CL V KA NOAPPEND NOPRINT FILE=ALTHGBTB.par
> $TABLE ID ETA1 ETA2 ETA3 NOAPPEND NOPRINT FILE=ALTHGBTB.eta
>
>
>
> On Tue, Jul 9, 2019 at 1:27 AM Luann Phillips <lu...@cognigencorp.com>
> wrote:
>
>> Vichapat,
>>
>> I just had another thought. You may want to check CL/F as a function of
>> time post transplant. As an initial, look you could try
>> CL=BLCL + BLCL*MAX*TIME/(TIME50+TIME)
>> where BLCL would be CL/F when TIME=0 or baseline CL
>> MAX = maximum proportional increase in CL relative to Baseline
>> TIME50=the time since transplot required to achieve 50% of the maximum
>> value of CL/F post transplant.
>>
>> If this shows significant improvement in model fit, then you should try a
>> model with continuous time in the $DES block.
>>
>> Best regards,
>> Luann
>>
>> ------------------------------
>> *From: *"Vichapat Tharanon" <vichapa...@gmail.com>
>> *To: *"nmusers" <nmusers@globomaxnm.com>
>> *Sent: *Monday, July 8, 2019 10:21:12 AM
>> *Subject: *[NMusers] Is it possible that IIV (%CV) of final model was
>> higher than IIV of base model?
>>
>> Dear All,
>>
>>
>> I am a hospital pharmacist and I am working on NONMEM as a
>> new user. I have modeled the oral immediate-released tacrolimus (Prograf)
>> in adult liver transplant patients.
>>
>> Most of the data were trough concentration (about 1170 levels) from routine
>> monitoring tacrolimus data in the period of first day post-transplantation
>> to 6 months. The model was constructed by NONMEM 7.2 using FOCE
>> INTERACTION methods with the subroutines ADVAN2 TRANS2 (one compartment
>> model with linear absorption and elimination). The ka could not be
>> estimated and then was fixed at 4.48 h-1. The IIIV and RUV were
>> described by exponential and additive error model, respectively. Forward
>> addition of a liver enzyme (ALT), Hemoglobin and total bilirubin (TB) on
>> CL/F reduced OFV significantly (delta OFV ~98, 42, 28, respectively) but
>> IIV of CL/F was increased from 37.2% to 38.1%. It was found that no
>> significant covariates influenced to V/F but IIV of V/F was also
>> increased from 55% to 63%. Residual variability was reduced from a SD of
>> 2.80 to 2.65, when compared final model and base model.
>>
>> I feel uncomfortable with these findings. Is it possible
>> that IIV of CL/F and V/F were rising after adding the significant
>> covariates whereas %RSE of the CL/F and V/F estimate as well as IIV of CL/F
>> and IIV of V/F in final model were slightly decreasing. May I have your
>> comment or suggestion; I would really appreciate it. Thank you in
>> advance.
>>
>> Best regards,
>>
>> Pete
>>
>
>
