Re: [NMusers] Reducing ETAs actually decreased OFV

2013-08-26 Thread Xinting Wang 
Dear Bill,
Appreciate your reply a lot. The issue is from KA. Adding KA or not did
have this problem. However, regarding your statement "it is rare to have
enough data to fit true IIV", can you explain more about this. My data set
is from Phase I studies, and I thought this should be enough for this
simulation.

Dear Leonid,
Thanks very much for your detailed suggestion. I followed the steps you
listed above, and did find that the OFV decreased in step 2, just as you
predicted. Then using the estimation to replace the initial values for all
of the THETA, OMEGA and SIGMA, the OFV stabilized. However, I am curious
about the explanation for this. And, is this a universal method for
estimation of initial values? Thank you.

The change of OFV was around 100 (the OFV was ~114300). I am pasting the
OMEGA matrix below for your information.

ETA1  ETA2  ETA3  ETA4  ETA5

 ETA1
+4.08E-02

 ETA2
+0.00E+00  1.57E-01

 ETA3
+0.00E+00  0.00E+00  1.30E-01

 ETA4
+0.00E+00  0.00E+00  0.00E+00  4.07E-01

 ETA5
+0.00E+00  0.00E+00  0.00E+00  0.00E+00  2.19E-02

ETA5 (0.0219) is the one caused the problem.

Best Regards


On 26 August 2013 07:06, Leonid Gibiansky  wrote:

> Hi Xinting,
> You should be able to do it. Let's check it again this way
> 1. You run the model with all ETAs included, but one ETA (the one that was
> excluded in the reduced model) is fixed to zero. You should be able to
> reproduce your "reduced ETA" result (OF)
> 2. You take the same control stream, and set all initial values to the
> final parameter estimates of model (1) above, except you use the small
> value (may be not 0.01 but 0.01) as the initial value of the ETA that
> was fixed to zero in model (1).
>
> Model (2) is the not-reduced model, and it's OF should be less or equal to
> the OF of model (1). If this is not the case, increase the number of
> significant digits in the initial estimates of model (2) - take those from
> the final estimates of model 1.
>
> Without data, it is very difficult to offer more specific advice.
>
> Also, what is the magnitude of the OF change? What is the estimate of the
> OMEGA for the ETA in question?
>
> Regards,
>
> Leonid
>
>
>
>
> --**
> Leonid Gibiansky, Ph.D.
> President, QuantPharm LLC
> web:www.quantpharm.com
> e-mail: LGibiansky at quantpharm.com
> tel:(301) 767 5566
>
>
>
> On 8/25/2013 8:42 AM, Xinting Wang wrote:
>
>> Dear Leonid,
>>
>> I tried with your method and found the same result. The initial
>> estimation of the added ETA was set at 0.01, and the result showed an
>> increase of OFV. Please see below the $PK part of the control file for
>> more information. Many thanks.
>>
>> Dear Bill,
>>
>> Could you please explain that in a little bit more detail? I am pasting
>> the $PK part of the control file in case you could find the useful
>> information. Thanks a lot.
>>
>> $PK
>>
>> FA1=0
>> FA2=0
>> FA3=0
>> FA4=0
>>
>> IF(DOSE.EQ.250) THEN
>> FA1=1
>> ENDIF
>>
>> IF(DOSE.EQ.500) THEN
>> FA2=1
>> ENDIF
>>
>> IF(DOSE.EQ.850) THEN
>> FA3=1
>> ENDIF
>>
>> IF(DOSE.EQ.1000) THEN
>> FA4=1
>> ENDIF
>>
>> F1=FA1+FA2*THETA(6)+FA3*THETA(**7)+FA4*THETA(8)
>>
>> TVCL=THETA(1)
>> TVV2=THETA(2)
>> TVKA=THETA(3)
>> TVQ=THETA(4)
>> TVV3=THETA(5)
>>
>> CL=TVCL*EXP(ETA(1))
>> V2=TVV2*EXP(ETA(2))
>> KA=TVKA*EXP(ETA(5))
>> Q=TVQ*EXP(ETA(3))
>> V3=TVV3*EXP(ETA(4))
>>
>>
>> S2=V2/1000
>> S3=V3/1000
>>
>>
>> $ERROR
>>
>> IPRE=F
>>
>> IRES=DV-IPRE
>>
>> W=F
>>
>> IF(W.EQ.0) W = 1
>>
>> IWRE  = IRES/W
>>
>> Y=F*(1+EPS(1))+EPS(2)
>>
>> Best Regards
>>
>>
>> On 12 August 2013 20:50, Denney, William S. > >
>> wrote:
>>
>> Hi Xinting,
>>
>> In a few rare cases, I've seen this happen if the model is
>> approaching nonconvergence.  In those cases, typically the RSE on
>> one or more parameters will increase and the ratio of max to min
>> eigenvalues will increase substantially.  Are you seeing either of
>> these?
>>
>> Thanks,
>>
>> Bill
>>
>> On Aug 11, 2013, at 21:56, "Leonid Gibiansky"
>> > >
>> wrote:
>>
>> Xinting,
>> Try to start from the initial conditions of your "reduced" model but
>> add that "reduced" ETA with the corresponding OMEGA equal to 0.01 or
>> other small number. If the control stream code is correct, the
>> objective function should decrease or retain the same value.
>> Leonid
>>
>> --**
>> Leonid Gibiansky, Ph.D.
>> President, QuantPharm LLC
>> web: www.quantpharm.com 
>>
>> e-mail: LGibiansky at quantpharm.com 
>> tel: (301) 767 5566 
>>
>>
>>
>> On 8/10/2013 10:23 PM, Xinting Wang wrote:
>>  > Dear all,
>>  >
>>  > Does anyone witnessed such a phenomenon in NONMEM as when you
>> reduced an
>>  > ETA, the OFV value, rather

RE: [NMusers] Reducing ETAs actually decreased OFV

2013-08-26 Thread Denney, William S. 
Hi Xinting,

This is a rather broad (and often highly-opinionated) topic.  At the highest 
level, you can only fit parameters in a model where you have enough data to 
estimate the parameter.  A simple example is that if you have data that you 
want to fit an Emax model to with measurements only up to the EC10, you don't 
have enough data to estimate Emax and ED50; it will look fully linear.

Due to data variability and the fact that Q and V3 are less correlated with the 
measurements than other parameters (Ka, V2, and CL have a stronger effect on 
the measurements than Q and V3), the estimates will be more difficult.  A good 
example of how to evaluate this would be what Peter Bonate just suggested: do 
likelihood profiling on each of the parameters (especially the ETAs) to 
estimate the certainty (peakedness) or uncertainty (flatness) in the parameter 
estimates.

Thanks,

Bill

From: Xinting Wang [mailto:wxinting1...@gmail.com]
Sent: Monday, August 26, 2013 9:27 AM
To: Leonid Gibiansky
Cc: nmusers@globomaxnm.com; Denney, William S.
Subject: Re: [NMusers] Reducing ETAs actually decreased OFV

Dear Bill,
Appreciate your reply a lot. The issue is from KA. Adding KA or not did have 
this problem. However, regarding your statement "it is rare to have enough data 
to fit true IIV", can you explain more about this. My data set is from Phase I 
studies, and I thought this should be enough for this simulation.

Dear Leonid,
Thanks very much for your detailed suggestion. I followed the steps you listed 
above, and did find that the OFV decreased in step 2, just as you predicted. 
Then using the estimation to replace the initial values for all of the THETA, 
OMEGA and SIGMA, the OFV stabilized. However, I am curious about the 
explanation for this. And, is this a universal method for estimation of initial 
values? Thank you.
The change of OFV was around 100 (the OFV was ~114300). I am pasting the OMEGA 
matrix below for your information.

ETA1  ETA2  ETA3  ETA4  ETA5

 ETA1
+4.08E-02

 ETA2
+0.00E+00  1.57E-01

 ETA3
+0.00E+00  0.00E+00  1.30E-01

 ETA4
+0.00E+00  0.00E+00  0.00E+00  4.07E-01

 ETA5
+0.00E+00  0.00E+00  0.00E+00  0.00E+00  2.19E-02
ETA5 (0.0219) is the one caused the problem.
Best Regards

On 26 August 2013 07:06, Leonid Gibiansky 
mailto:lgibian...@quantpharm.com>> wrote:
Hi Xinting,
You should be able to do it. Let's check it again this way
1. You run the model with all ETAs included, but one ETA (the one that was 
excluded in the reduced model) is fixed to zero. You should be able to 
reproduce your "reduced ETA" result (OF)
2. You take the same control stream, and set all initial values to the final 
parameter estimates of model (1) above, except you use the small value (may be 
not 0.01 but 0.01) as the initial value of the ETA that was fixed to zero 
in model (1).

Model (2) is the not-reduced model, and it's OF should be less or equal to the 
OF of model (1). If this is not the case, increase the number of significant 
digits in the initial estimates of model (2) - take those from the final 
estimates of model 1.

Without data, it is very difficult to offer more specific advice.

Also, what is the magnitude of the OF change? What is the estimate of the OMEGA 
for the ETA in question?

Regards,

Leonid




--
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web:www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel:(301) 767 5566


On 8/25/2013 8:42 AM, Xinting Wang wrote:
Dear Leonid,

I tried with your method and found the same result. The initial
estimation of the added ETA was set at 0.01, and the result showed an
increase of OFV. Please see below the $PK part of the control file for
more information. Many thanks.

Dear Bill,

Could you please explain that in a little bit more detail? I am pasting
the $PK part of the control file in case you could find the useful
information. Thanks a lot.

$PK

FA1=0
FA2=0
FA3=0
FA4=0

IF(DOSE.EQ.250) THEN
FA1=1
ENDIF

IF(DOSE.EQ.500) THEN
FA2=1
ENDIF

IF(DOSE.EQ.850) THEN
FA3=1
ENDIF

IF(DOSE.EQ.1000) THEN
FA4=1
ENDIF

F1=FA1+FA2*THETA(6)+FA3*THETA(7)+FA4*THETA(8)

TVCL=THETA(1)
TVV2=THETA(2)
TVKA=THETA(3)
TVQ=THETA(4)
TVV3=THETA(5)

CL=TVCL*EXP(ETA(1))
V2=TVV2*EXP(ETA(2))
KA=TVKA*EXP(ETA(5))
Q=TVQ*EXP(ETA(3))
V3=TVV3*EXP(ETA(4))


S2=V2/1000
S3=V3/1000


$ERROR

IPRE=F

IRES=DV-IPRE

W=F

IF(W.EQ.0) W = 1

IWRE  = IRES/W

Y=F*(1+EPS(1))+EPS(2)

Best Regards


On 12 August 2013 20:50, Denney, William S. 
mailto:william.s.den...@pfizer.com>
>> wrote:

Hi Xinting,

In a few rare cases, I've seen this happen if the model is
approaching nonconvergence.  In those cases, typically the RSE on
one or more parameters will increase and the ratio of max to min
eigenvalues will increase substantially.  Are

Re: [NMusers] Time-varing covariate

2013-08-26 Thread J.H. Proost 

Dear Nick,

In your reply to Siwei, you proposed the following code:


$PK
; CL=(CLnon-renal*f(age) + CLrenal*f(renal_function)) * allometric WT
CL=(THETA(1)*EXP(THETA(2)*(AGE-40)) + THETA(3)*CLCR/100)*(WT/70)**0.75


I would like to make a comment on the coding of the renal function. If CLCR 
is expressed in ml/min, the expression THETA(3)*CLCR/100 represents the 
renal clearance of the individual with renal function CLCR, where THETA(3) 
is the drug's renal clearance for an individual with creatinine clearance of 
100 ml/min (a reasonable value for an average individual but not a standard 
value).
In my opinion, the allometric term should not be applied on this renal part 
of clearance. Therefore I suggest to use the following code line:


; CL= CLnon-renal*f(age)*allometric WT  + CLrenal*f(renal_function)

CL= THETA(1)*EXP(THETA(2)*(AGE-40))*(WT/70)**0.75  +  THETA(3)*CLCR/100

If CLCR is expressed in ml/min/1.73m2 (the 'normalized renal function', 
often used in lab results, e.g. in the MDRD equation; useful for clinical 
judgement of renal function, but not for modeling or dosing purposes), your 
code could be used, but in that case I would prefer to first convert CLCR to 
ml/min (the 'true renal function') and then use the above code line.


Note: Units of THETA(1) and THETA(3) are here in ml/min; for using the more 
conventional L/h, multiplication by 60/1000 should be added.


best regards,

Hans Proost


Johannes H. Proost
Dept. of Pharmacokinetics, Toxicology and Targeting
University Centre for Pharmacy
Antonius Deusinglaan 1
9713 AV Groningen, The Netherlands

tel. 31-50 363 3292
fax 31-50 363 3247

Email: j.h.pro...@rug.nl

Re: [NMusers] Reducing ETAs actually decreased OFV

2013-08-26 Thread Leonid Gibiansky 
I am not sure that you need likelihood profiling or any other 
sophisticated procedures to study this particular problem. You can look 
at relative standard errors of the parameter estimates: if one of the 
ETAs is poorly estimated, this is the candidate for removal. For 
two-compartment models, it is rarely possible to estimate ETAs on 
peripheral compartment, and at least one of those can be removed (usually).


If the goal is to describe the data, you look for the simplest model 
that allow you to fit the data. You may start with the model with all 
random effects, but then try to reduce the number of random effect 
(unless you use new IMP/SAEM/BAYES type procedures) to arrive at the 
simpler model.  You may use OF as a guide: if OF drop is small when you 
remove the ETA, this ETA does not contribute to the fit (and the model 
can equally well fit the data without this particular ETA). Alternative 
procedure is to compare full (with ETAs) and reduced (with one ETA fixed 
to zero) model using various diagnostic plots procedure (VPC in 
particular), or plots of one model versus the other model: PRED vs PRED 
and IPRED vs IPRED (where PRED and IPRED belog to two models that you 
are comparing). If these plots looks like identity lines (both in normal 
and log axes), you can safely use simpler model, especially if VPC 
results are similar or identical.


As to the specific procedure that allowed you to fix the strange OF 
behavior, even the simple problems (like two-compartment model that was 
used) are highly nonlinear, and gradient methods cannot guarantee the 
global minimum. The solution (local minimum) may depend on initial 
conditions. By starting from the solution of the reduced problem, you 
put the model in the vicinity of the correct local minimum, while when 
you started from the larger model, it converged to the different 
minimum. This is not a universal procedure, but it helps time to time if 
the model has difficulties finding the solution.


Regards,
Leonid


--
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web:www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel:(301) 767 5566



On 8/26/2013 9:58 AM, Denney, William S. wrote:

Hi Xinting,

This is a rather broad (and often highly-opinionated) topic.  At the
highest level, you can only fit parameters in a model where you have
enough data to estimate the parameter.  A simple example is that if you
have data that you want to fit an Emax model to with measurements only
up to the EC10, you don’t have enough data to estimate Emax and ED50; it
will look fully linear.

Due to data variability and the fact that Q and V3 are less correlated
with the measurements than other parameters (Ka, V2, and CL have a
stronger effect on the measurements than Q and V3), the estimates will
be more difficult.  A good example of how to evaluate this would be what
Peter Bonate just suggested: do likelihood profiling on each of the
parameters (especially the ETAs) to estimate the certainty (peakedness)
or uncertainty (flatness) in the parameter estimates.

Thanks,

Bill

*From:*Xinting Wang [mailto:wxinting1...@gmail.com]
*Sent:* Monday, August 26, 2013 9:27 AM
*To:* Leonid Gibiansky
*Cc:* nmusers@globomaxnm.com; Denney, William S.
*Subject:* Re: [NMusers] Reducing ETAs actually decreased OFV

Dear Bill,

Appreciate your reply a lot. The issue is from KA. Adding KA or not did
have this problem. However, regarding your statement "it is rare to have
enough data to fit true IIV", can you explain more about this. My data
set is from Phase I studies, and I thought this should be enough for
this simulation.

Dear Leonid,

Thanks very much for your detailed suggestion. I followed the steps you
listed above, and did find that the OFV decreased in step 2, just as you
predicted. Then using the estimation to replace the initial values for
all of the THETA, OMEGA and SIGMA, the OFV stabilized. However, I am
curious about the explanation for this. And, is this a universal method
for estimation of initial values? Thank you.

The change of OFV was around 100 (the OFV was ~114300). I am pasting the
OMEGA matrix below for your information.

 ETA1  ETA2  ETA3  ETA4  ETA5

  ETA1
+4.08E-02

  ETA2
+0.00E+00  1.57E-01

  ETA3
+0.00E+00  0.00E+00  1.30E-01

  ETA4
+0.00E+00  0.00E+00  0.00E+00  4.07E-01

  ETA5
+0.00E+00  0.00E+00  0.00E+00  0.00E+00  2.19E-02

ETA5 (0.0219) is the one caused the problem.

Best Regards

On 26 August 2013 07:06, Leonid Gibiansky mailto:lgibian...@quantpharm.com>> wrote:

Hi Xinting,
You should be able to do it. Let's check it again this way
1. You run the model with all ETAs included, but one ETA (the one that
was excluded in the reduced model) is fixed to zero. You should be able
to reproduce your "reduced ETA" result (OF)
2. You take the same control stream, and set all initial values to the
final parameter estimates of model (1) ab

[NMusers] Renal function as a covariate

2013-08-26 Thread Nick Holford 

Hans,

I agree with you that what I wrote was not properly explained. Thanks 
for pointing this out. My previous comment was related to a question 
about time-varying covariates. You raise other important issues about 
how to use renal function as a covariate so I have re-named this thread 
and offer the following explanation.


CLCR, as I proposed using it, should be a size standardized value  -- I 
should have used the term CLCRstd to make this clearer.


I suggest standardizing a prediction, such as that obtained from the 
Cockcroft & Gault formula (CLCRCG), to a standard size of 70 kg as follows:


CLCRstd=CLCRCG*70/WT

then using it as I proposed in the equation below. Note that WT is total 
body weight when used with CLCRCG because this formula was developed 
based on creatinine production rate expressed per kg of total body weight.


f(age) is an empirical function centered on an age of 40 years. It is of 
exponential form to avoid extrapolation to negative values. 
f(renal_function) is CLCRstd/100 where 100 mL/min is a 'normal' CLCR for 
a 70 kg person. The model assumes CLrenal is linearly related to renal 
function but more complex models can easily be implemented.


CL=(CLnon-renal*f(age) + CLrenal*  f(renal_function) )   
* allometric WT
CL=(THETA(1)*EXP(THETA(2)*(AGE-40)) + THETA(3)  * CLCRstd/100   
)   * (WT/70)**0.75


This approach applies size standardization consistently to both 
non-renal and renal components of clearance (see for example Mould et 
al. 2002, Matthews et al. 2006 for applications).


There is a problem with the method you propose of using non-size 
standardized CLCR to account for a component of clearance. Even if there 
is no renal elimination of a drug, then if there is a reasonable 
distribution of size in the sample being studied, then THETA(3) may 
appear to be different from zero because it reflects differences in size 
not just renal function.


Note that using surface area as a form of size standardization for 
glomerular filtration rate has no theoretical nor experimental support 
when compared to theory based allometry (Rhodin et al. 2009). So I do 
not agree with standardizing CLCR to 1.73 m^2. I know this is frequently 
done but in fact this is just based on tradition and an out of date 
theory of scaling based on surface area (see Anderson & Holford 2008). 
The MDRD method of predicting glomerular filtration rate is a 
statistical absurdity which does not include any measurement of size for 
its prediction. I would certainly not recommend using it for any 
scientific purpose.


The choice of units for CLCR is somewhat context dependent. The commonly 
used Cockcroft & Gault method (CLCRCG) returns values in mL/min so that 
is why I chose 100 mL/min for a 70 kg person. I would agree that in 
general it is better to report clearances as L/h/70kg.


Describing CLCR as 'renal function' is also traditional but I prefer to 
calculate the ratio of the predicted CLCRstd in an individual to a 
standard 'normal' value to obtain a dimensionless renal function 
variable that is independent of size and is more directly related to the 
function of the kidneys. This renal function value also gets around the 
problems of units chosen to express CLCR as long as consistent values 
are chosen to compare the individual prediction with the 'normal' value.


Please look at this recent review of the use of standards for PK 
Parameters which discusses this issue and also demonstrates how to 
account for maturation of renal functionfor ages less than 2 years 
(Holford, Yeo, Anderson 2013).


Best wishes,

Nick

1.Mould DR, Holford NH, Schellens JH, Beijnen JH, Hutson PR, Rosing 
H, et al. Population pharmacokinetic and adverse event analysis of 
topotecan in patients with solid tumors. Clinical Pharmacology & 
Therapeutics. 2002;71(5):334-48.
2.Matthews I, Kirkpatrick C, Holford N. Quantitative justification 
for target concentration intervention--parameter variability and 
predictive performance using population pharmacokinetic models for 
aminoglycosides. Br J Clin Pharmacol. 2004;58(1):8-19.
3.Rhodin MM, Anderson BJ, Peters AM, Coulthard MG, Wilkins B, Cole 
M, et al. Human renal function maturation: a quantitative description 
using weight and postmenstrual age. Pediatr Nephrol. 2009;24(1):67-76.
4.Anderson BJ, Holford NH. Mechanism-based concepts of size and 
maturity in pharmacokinetics. Annu Rev Pharmacol Toxicol. 2008;48:303-32.
5.Holford N, Heo YA, Anderson B. A pharmacokinetic standard for 
babies and adults. J Pharm Sci. 2013;102(9):2941-52.




On 26/08/2013 4:09 p.m., J.H. Proost wrote:

Dear Nick,

In your reply to Siwei, you proposed the following code:


$PK
; CL=(CLnon-renal*f(age) + CLrenal*f(renal_function)) * allometric WT
CL=(THETA(1)*EXP(THETA(2)*(AGE-40)) + THETA(3)*CLCR/100)*(WT/70)**0.75


I would like to make a comment on the coding of the renal function. If 
CLCR is expressed in ml/min, the expression THETA(3)*CL

Re: [NMusers] Time-varing covariate

2013-08-26 Thread siwei Dai 
Hi, Sebastien, Bill, Nick, Leonid, Mats and Hans:

Thank you all very much for the suggestions and nice discussions. I enjoyed
to learn from this thread and I am very clear how this should be handled
now. I believe this thread also provided a nice record for other new folks
like me to learn from.

Thanks a lot.

Best regards,

Siwei


On Fri, Aug 23, 2013 at 1:10 PM, Nick Holford wrote:

> Siwei,
>
> I don't know why you think this complicated. Suppose you have age (AGE) as
> a covariate. This must of course be a time varying covariate if it is
> intended to be the current age. And you might have weight (WT) or
> creatinine clearance (CLCR) as covariates which typically change with time.
> So just code the $INPUT data items and use them as you wish e.g.
>
> $INPUT ID TIME AGE WT CLCR etc
> ...
>
> $PK
> ; CL=(CLnon-renal*f(age) + CLrenal*f(renal_function)) * allometric WT
> CL=(THETA(1)*EXP(THETA(2)*(**AGE-40)) + THETA(3)*CLCR/100)*(WT/70)**0.**75
>
> EVID=4 has nothing to do with using time varying covariates.
>
> Perhaps you could explain more clearly what your problem is and why you
> think it is complicated to use time varying covariates?
>
> Best wishes,
>
> Nick
>
>
> On 23/08/2013 6:00 p.m., siwei Dai wrote:
>
>> 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
>>
>
> --
> 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
> office:+64(9)923-6730 mobile:NZ +64(21)46 23 53 FR +33(7)85 36 84 99
> email: n.holf...@auckland.ac.nz
> http://holford.fmhs.auckland.**ac.nz/
>
> Holford NHG. Disease progression and neuroscience. Journal of
> Pharmacokinetics and Pharmacodynamics. 2013;40:369-76
> http://link.springer.com/**article/10.1007/s10928-013-**9316-2
> Holford N, Heo Y-A, Anderson B. A pharmacokinetic standard for babies and
> adults. J Pharm Sci. 2013: http://onlinelibrary.wiley.**
> com/doi/10.1002/jps.23574/**abstract
> Holford N. A time to event tutorial for pharmacometricians. CPT:PSP.
> 2013;2: 
> http://www.nature.com/psp/**journal/v2/n5/full/psp201318a.**html
> Holford NHG. Clinical pharmacology = disease progression + drug action.
> British Journal of Clinical Pharmacology. 2013:
> http://onlinelibrary.wiley.**com/doi/10./bcp.12170/**abstract
>
>
>

[NMusers] Workshop on PK and PD Modeling Biologics in Baltimore, MD

2013-08-26 Thread Leonid Gibiansky 
I am sorry for multiple posting, but by mistake, this workshop was 
listed as closed on ICON web site for the last two weeks while it is 
actually open for enrollment. You can register by e-mailing


Lisa R. Wilhelm
lisa.wilh...@iconplc.com
Tel: 410-696-3060

Thanks
Leonid

---
Second announcement:

Leonid and Ekaterina Gibiansky (QuantPharm LLC) will present the
workshop on Modeling of Biologics with Target-Mediated Disposition in
Maryland (near Baltimore), September 13, 2013.

The workshop provides an overview of the PK of biologics, introduces
target-mediated drug disposition (TMDD) modeling concepts, and discusses
applications of TMDD modeling to drug development of biologics. Latest
developments such as modeling of antibody-drug conjugates and
immunogenicity, will be presented. NONMEM codes, inputs and outputs for
TMDD modeling are provided to the participants.

More details can be found here:

 http://www.quantpharm.com/Workshop.html

The link below contains registration details:

http://www.iconplc.com/news-events/events/workshops/a-1-day-workshop-on-model/index.xml

Thanks!
Leonid

--
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web:www.quantpharm.com
e-mail: LGibiansky at quantpharm.com

RE: [NMusers] Time-varing covariate

2013-08-26 Thread Mats Karlsson 
Dear Siwei,

 

If you have a time-varying covariate, you may want to entertain the extended
models possible/necessary for time-varying, as opposed to time-constant,
covariates. See Wählby et al “Models for time-varying covariates in
population pharmacokinetic-pharmacodynamic analysis.” Br J Clin Pharmacol.
2004 Oct;58(4):367-77.

 

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

 
www.farmbio.uu.se/research/researchgroups/pharmacometrics/

 

From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On
Behalf Of siwei Dai
Sent: 26 August 2013 18:13
To: nmusers@globomaxnm.com
Subject: Re: [NMusers] Time-varing covariate

 

Hi, Sebastien, Bill, Nick, Leonid, Mats and Hans:

 

Thank you all very much for the suggestions and nice discussions. I enjoyed
to learn from this thread and I am very clear how this should be handled
now. I believe this thread also provided a nice record for other new folks
like me to learn from.

 

Thanks a lot. 

 

Best regards, 

 

Siwei

 

On Fri, Aug 23, 2013 at 1:10 PM, Nick Holford 
wrote:

Siwei,

I don't know why you think this complicated. Suppose you have age (AGE) as a
covariate. This must of course be a time varying covariate if it is intended
to be the current age. And you might have weight (WT) or creatinine
clearance (CLCR) as covariates which typically change with time. So just
code the $INPUT data items and use them as you wish e.g.

$INPUT ID TIME AGE WT CLCR etc
...

$PK
; CL=(CLnon-renal*f(age) + CLrenal*f(renal_function)) * allometric WT
CL=(THETA(1)*EXP(THETA(2)*(AGE-40)) + THETA(3)*CLCR/100)*(WT/70)**0.75

EVID=4 has nothing to do with using time varying covariates.

Perhaps you could explain more clearly what your problem is and why you
think it is complicated to use time varying covariates?

Best wishes,

Nick



On 23/08/2013 6:00 p.m., siwei Dai wrote:

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

 

-- 
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
office:+64(9)923-6730   mobile:NZ +64(21)46 23 53
  FR +33(7)85 36 84 99
 
email: n.holf...@auckland.ac.nz
http://holford.fmhs.auckland.ac.nz/

Holford NHG. Disease progression and neuroscience. Journal of
Pharmacokinetics and Pharmacodynamics. 2013;40:369-76
http://link.springer.com/article/10.1007/s10928-013-9316-2
Holford N, Heo Y-A, Anderson B. A pharmacokinetic standard for babies and
adults. J Pharm Sci. 2013:
http://onlinelibrary.wiley.com/doi/10.1002/jps.23574/abstract
Holford N. A time to event tutorial for pharmacometricians. CPT:PSP. 2013;2:
http://www.nature.com/psp/journal/v2/n5/full/psp201318a.html
Holford NHG. Clinical pharmacology = disease progression + drug action.
British Journal of Clinical Pharmacology. 2013:
http://onlinelibrary.wiley.com/doi/10./bcp.12170/abstract