Dear Luann,
"How did you determine that ALT, HGB, and TB should be in the model
for TVCL?" I have determined these covariates as significant covariates by
stepwise forward selection and backward elimination. Sincerely, I really
concerned that I answered you in the right way?
kind regards,
Pete
On Wed, Jul 10, 2019 at 1:23 AM Luann Phillips <lu...@cognigencorp.com>
wrote:
> Hi Vichapat,
>
> I was just asking which method that you had used. Some people do use the
> baseline value for the whole dataset even for long studies . I disagree
> with this method. I think time-varying covariate values should always be
> used for long study periods. It sounds like the way you built your data is
> fine.
> The ctl stream looks the same whether you use stationary or time-varying
> covariates. When NM is fitting the model, it uses which ever covariate
> value is available.
>
> As the differential equation solver in NONMEM steps from TIME=N to
> TIME=N+1, the covariate value from TIME=N+1 is used (see example below)
>
> Example:
>
> TIME=0 AMT=40 DV=. COVAR=55
> TIME=11.833 AMT=. DV=25 COVAR=55
> TIME=12 AMT=40 DV=. COVAR=55
> TIME=23.833 AMT=. DV=30 COVAR=55
> TIME=24 AMT=40 DV=. COVAR=60
> TIME=35.833 AMT=. DV=25 COVAR=60
> TIME=36 AMT=40 DV=. COVAR=60
> TIME=47.833 AMT=. DV=27 COVAR=60
> etc.
>
> As NONMEM steps from TIME=0 to TIME=11.833 the value of COVAR=55 from the
> TIME=11.833 record is used
> As NONMEM steps from TIME=11.833 to TIME=12 the value of COVAR=55 from the
> TIME=12 record is used
> As NONMEM steps from TIME=12 to TIME=23.833 the value of COVAR=55 from the
> TIME=23.833 record is used
> As NONMEM steps from TIME=23.833 to TIME=24 the value of COVAR=60 from the
> TIME=24 record is used
> As NONMEM steps from TIME=24 to TIME=35.833 the value of COVAR=60 from the
> TIME=35.833 record is used
> As NONMEM steps from TIME=35.833 to TIME=36 the value of COVAR=60 from the
> TIME=36 record is used
> As NONMEM steps from TIME=36 to TIME=47.833 the value of COVAR=60 from the
> TIME=47.833 record is used
> etc.
>
> If the value of COVAR=55 for all records NONMEM still works the same way,
> it's just that the value of COVAR will never change.
>
> So in your case,
> TVCL=THETA(1)*EXP(THETA(4)*(ALT/388))*((HGB/10.50)**THETA(5))*((TB/4.7)**THETA(6))
> changes value every time that ALT, HGB, or TB changes value.
> The ETA(1) value remains the same for all observations within an
> individual but CL will still change with time because ALT, HGB, and TB
> change with time.
>
> How did you determine that ALT, HGB, and TB should be in the model for
> TVCL?
>
> Luann
>
>
> ------------------------------
> *From: *"Vichapat Tharanon" <vichapa...@gmail.com>
> *To: *"Luann Phillips" <luann.phill...@cognigencorp.com>
> *Cc: *"nmusers" <nmusers@globomaxnm.com>
> *Sent: *Tuesday, July 9, 2019 1:53:18 PM
> *Subject: *Re: [NMusers] Is it possible that IIV (%CV) of final model was
> higher than IIV of base model?
>
> 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
>>>
>>
>
