RE: [NMusers] Additive plus proportional error model for log-transform data

2016-06-02 Thread Mats Karlsson 
Dear Ahmad,

You don't havet o choose between normal or transformed concentrations in your 
error model, you can let NONMEM estimate the most appropriate transformation 
for you. Combining this with a power transform error model I think is likely to 
solve your problem. See
A strategy for residual error modeling incorporating scedasticity of variance 
and distribution shape.
Dosne AG, Bergstrand M, Karlsson MO.
J Pharmacokinet Pharmacodyn. 2016 Apr;43(2):137-51. doi: 
10.1007/s10928-015-9460-y. Epub 2015 Dec 17.

It is automated in PsN as "execute -dtbs ..."

Besst regaards,
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/<http://www.farmbio.uu.se/research/researchgroups/pharmacometrics/>

From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On 
Behalf Of Jakob Ribbing
Sent: Thursday, June 02, 2016 6:32 AM
To: Abu Helwa, Ahmad Yousef Mohammad - abuay010
Cc: nmusers@globomaxnm.com
Subject: Re: [NMusers] Additive plus proportional error model for log-transform 
data

Hi Ahmad,

The two error models are equivalent (only that with Leonids suggested code, the 
additive-on-log-transformed error term (TH16) is estimated on variance scale, 
instead of standard deviation scale (approximate CV).
This inflated error rates for very low concentrations is what you get for 
additive+proportional on the log transformed scale, and I believe that has been 
discussed on nmusers previously as well, many years ago.
You could possibly use a cut-off for when lower IPRE should not lead to higher 
residual errors, but why not move to additive + proportional for the original 
concentration scale?

Also, this implementation may be unfortunate:
Y=(1-FLAG)*IPRE + W*EPS(1)

Effectively, when concentration predictions are zero (FLAG=1), e.g. for 
pre-dose samples or before commence of absorption, then you set the 
concentration prediction to EXP(1)=3.14 concentration units.
Depending on what concentration scale you work on (i.e. if BLQ is much higher 
than this) it may be OK, but otherwise not.
Instead of applying a flag, just set IPRE to a negative value (low in relation 
to LOG(BLQ)), if you want to stay on the log-transformed scale.

I hope this helps to solve your problem.

Best regards

Jakob



Jakob Ribbing, Ph.D.

Senior Consultant, Pharmetheus AB



Cell/Mobile:+46 (0)70 514 33 77

jakob.ribb...@pharmetheus.com<mailto:jakob.ribb...@pharmetheus.com>

www.pharmetheus.com<http://www.pharmetheus.com/>



Phone, Office:+46 (0)18 513 328

Uppsala Science Park, Dag Hammarskjölds väg 52B

SE-752 37 Uppsala, Sweden



This communication is confidential and is only intended for the use of the 
individual or entity to which it is directed. It may contain information that 
is privileged and exempt from disclosure under applicable law. If you are not 
the intended recipient please notify us immediately. Please do not copy it or 
disclose its contents to any other person.



On 02 Jun 2016, at 04:27, Abu Helwa, Ahmad Yousef Mohammad - abuay010 
<ahmad.abuhe...@mymail.unisa.edu.au<mailto:ahmad.abuhe...@mymail.unisa.edu.au>> 
wrote:


Dear NMusers,

I am developing a PK model using log-transformed single-dose oral data. My 
question relates to using combined error model for log-transform data.

I have read few previous discussions on NMusers regarding this, which were 
really helpful, and I came across two suggested formulas (below) that I tested 
in my PK models.  Both formulas had similar model fits in terms of OFV (OFV 
using Formula 2 was one unit less than OFV using Formula1) with slightly 
changed PK parameter estimates. My issue with these formulas is that the model 
simulates very extreme concentrations (e.g. upon generating VPCs) at the early 
time points (when drug concentrations are low) and at later time points when 
the concentrations are troughs. These simulated extreme concentrations are not 
representative of the model but a result of the residual error model structure.

My questions:
1.   Is there a way to solve this problem for the indicated formulas?
2.   Are the two formulas below equally valid?
3.   Is there an alternative formula that I can use which does not have 
this numerical problem?
4.   Any reference paper that discusses this subject?

Here are the two formulas:
1.   Formula 1: suggested by Mats Karlsson with fixing SIGMA to 1:
W=SQRT(THETA(16)**2+THETA(17)**2/EXP(IPRE)**2)

2.   Formula 2: suggested by Leonid Gibiansky with fixing SIGMA to 1:
W = SQRT(THETA(16)+ (THETA(17)/EXP(IPRE))**2  )

The way I apply it in my model is this:

FLAG=0 ;TO AVOID ANY CALCULATIONS OF LOG (0)
IF (F.EQ.0) FLAG=1
IPRE=LOG(F+FLAG)

W=SQRT(THETA(16)**2+THETA(17)**2/EXP(IPRE)**2) ;FORMULA 1

IRES=DV-IPRE
IWRES=IRE

Re: [NMusers] Additive plus proportional error model for log-transform data

2016-06-02 Thread Leonid Gibiansky 
I also like this version:

  W = SDL-(SDL-SDH)*TY/(SD50+TY)
  Y=LTY+W*EPS(1)

Here SDL is the standard deviation (in logs) at low concentrations, SDH is
the standard deviation at high concentrations, TY is the individual
prediction, LTY is LOG(TY). SIGMA should be fixed at 1

Leonid


On Wed, Jun 1, 2016 at 10:27 PM, Abu Helwa, Ahmad Yousef Mohammad -
abuay010  wrote:

> Dear NMusers,
>
>
>
> I am developing a PK model using log-transformed single-dose oral data. My
> question relates to using combined error model for log-transform data.
>
>
>
> I have read few previous discussions on NMusers regarding this, which were
> really helpful, and I came across two suggested formulas (below) that I
> tested in my PK models.  Both formulas had similar model fits in terms of
> OFV (OFV using Formula 2 was one unit less than OFV using Formula1) with
> slightly changed PK parameter estimates. My issue with these formulas is
> that the model simulates very extreme concentrations (e.g. upon generating
> VPCs) at the early time points (when drug concentrations are low) and at
> later time points when the concentrations are troughs. These simulated
> extreme concentrations are not representative of the model but a result of
> the residual error model structure.
>
>
>
> My questions:
>
> 1.   Is there a way to solve this problem for the indicated formulas?
>
> 2.   Are the two formulas below equally valid?
>
> 3.   Is there an alternative formula that I can use which does not
> have this numerical problem?
>
> 4.   Any reference paper that discusses this subject?
>
>
>
> Here are the two formulas:
>
> 1.   Formula 1: suggested by Mats Karlsson with fixing SIGMA to 1:
>
> W=SQRT(THETA(16)**2+THETA(17)**2/EXP(IPRE)**2)
>
>
>
> 2.   Formula 2: suggested by Leonid Gibiansky with fixing SIGMA to 1:
>
> W = SQRT(THETA(16)+ (THETA(17)/EXP(IPRE))**2  )
>
>
>
> The way I apply it in my model is this:
>
>
>
> FLAG=0 ;TO AVOID ANY CALCULATIONS OF LOG (0)
>
> IF (F.EQ.0) FLAG=1
>
> IPRE=LOG(F+FLAG)
>
>
>
> W=SQRT(THETA(16)**2+THETA(17)**2/EXP(IPRE)**2) ;FORMULA 1
>
>
>
> IRES=DV-IPRE
>
> IWRES=IRES/W
>
> Y=(1-FLAG)*IPRE + W*EPS(1)
>
>
>
> $SIGMA
>
> 1. FIX
>
>
>
> Best regards,
>
>
>
> Ahmad Abuhelwa
>
> School of Pharmacy and Medical Sciences
>
> University of South Australia- City East Campus
>
> Adelaide, South Australia
>
> Australia
>
>
>



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

Re: [NMusers] Additive plus proportional error model for log-transform data

2016-06-02 Thread Rong Chen 
Hi Ahmad,


This issue hasbeen discussed a lot and I'm afraid there's no consensus yet. 

To your question:

1.   Is there away to solve this problem for the indicated formulas?

As you said, thisproblem occurs at the early/later time points. In other words, 
it happenswhen prediction is relatively low. This is because there's an 
underlyingapproximation in the derivation of the two formulas:



Innon-transformed terms

Conc=F*EXP(SQRT(THETA(x)**2+THETA(y)**2/F**2)*EPS(1))

Assuming thatEXP(x)=1+x (for small x), you get

Conc=F*(1+SQRT(THETA(x)**2+THETA(y)**2/F**2)*EPS(1))

 Variance of thisexpression

Var(conc)=F**2*(THETA(x)**2+THETA(y)**2/F**2)=

               = F**2*THETA(x)**2+THETA(y)**2

 On the otherhand, for the error model

Y=Fexp(EPS1)+EPS2=F(1+EPS1)+EPS2

variance isequal to

F**2*OMEGA1+OMEGA2

 Thus, thesemodels are similar if not identical with

OMEGA1=THETA(x)**2,

OMEGA2=THETA(y)**2

---

So when F is rathersmall, the approximation of exp(x)=1+x doesn't workanymore. 
This may not be a problem when fitting your data.It may only occurwhen the 
prediction is extremely low, let say 10^-4. In opinion it's safe touse this 
formula in most cases. But if you are seeking a perfect answer,Jacob's 
suggestion may be the one.

 2.   Are the twoformulas below equally valid?

 As far asI'm concerned, these two are equally valid. I question the result 
that there'sa difference in OFV and estimates between the two.

 3.   Is there analternative formula that I can use which does not have 
this numerical problem?

 Maybe youcan try Stu's "double exponential error model": Y = LOG(F+M) 
+(F/(F+M))*ERR(1) + (M/(F+M))*ERR(2). 


Best regards,


Rong Chen

School ofPharmaceutical Science

Peking University

Beijing, China


  From: "Abu Helwa, Ahmad Yousef Mohammad - abuay010" 
<ahmad.abuhe...@mymail.unisa.edu.au>
 To: "nmusers@globomaxnm.com" <nmusers@globomaxnm.com> 
 Sent: Thursday, 2 June 2016, 10:27
 Subject: [NMusers] Additive plus proportional error model for log-transform 
data
  
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{margin-bottom:0cm;}#yiv5739009609 Dear NMusers,    I am developing a PK model 
using log-transformed single-dose oral data. My question relates to using 
combined error model for log-transform data.    I have read few previous 
discussions on NMusers regarding this, which were really helpful, and I came 
across two suggested formulas (below) that I tested in my PK models.  Both 
formulas had similar model fits in terms of OFV (OFV using Formula 2 was one 
unit less than OFV using Formula1) with slightly changed PK parameter 
estimates. My issue with these formulas is that the model simulates very 
extreme concentrations (e.g. upon generating VPCs) at the early time points 
(when drug concentrations are low) and at later time points when the 
concentrations are troughs. These simulated extreme concentrations are not 
representative of the model but a result of the residual error model structure. 
   My questions: 1.  Is there a way to solve this problem for the indicated 
fo

Re: [NMusers] Additive plus proportional error model for log-transform data

2016-06-01 Thread Jakob Ribbing 
Sorry, an error in what I wrote below: It should be  EXP(0)=1 concentration unit



Jakob Ribbing, Ph.D.

Senior Consultant, Pharmetheus AB



Cell/Mobile:+46 (0)70 514 33 77

jakob.ribb...@pharmetheus.com

www.pharmetheus.com



Phone, Office:  +46 (0)18 513 328

Uppsala Science Park, Dag Hammarskjölds väg 52B

SE-752 37 Uppsala, Sweden



This communication is confidential and is only intended for the use of the 
individual or entity to which it is directed. It may contain information that 
is privileged and exempt from disclosure under applicable law. If you are not 
the intended recipient please notify us immediately. Please do not copy it or 
disclose its contents to any other person.





On 02 Jun 2016, at 06:31, Jakob Ribbing  wrote:

> Hi Ahmad,
> 
> The two error models are equivalent (only that with Leonids suggested code, 
> the additive-on-log-transformed error term (TH16) is estimated on variance 
> scale, instead of standard deviation scale (approximate CV).
> This inflated error rates for very low concentrations is what you get for 
> additive+proportional on the log transformed scale, and I believe that has 
> been discussed on nmusers previously as well, many years ago.
> You could possibly use a cut-off for when lower IPRE should not lead to 
> higher residual errors, but why not move to additive + proportional for the 
> original concentration scale?
> 
> Also, this implementation may be unfortunate:
>> Y=(1-FLAG)*IPRE + W*EPS(1)
> Effectively, when concentration predictions are zero (FLAG=1), e.g. for 
> pre-dose samples or before commence of absorption, then you set the 
> concentration prediction to EXP(1)=3.14 concentration units.
> 
> Depending on what concentration scale you work on (i.e. if BLQ is much higher 
> than this) it may be OK, but otherwise not.
> Instead of applying a flag, just set IPRE to a negative value (low in 
> relation to LOG(BLQ)), if you want to stay on the log-transformed scale.
> 
> I hope this helps to solve your problem.
> 
> Best regards
> 
> Jakob
> 
> 
> 
> Jakob Ribbing, Ph.D.
> 
> Senior Consultant, Pharmetheus AB
> 
> 
> 
> Cell/Mobile:  +46 (0)70 514 33 77
> 
> jakob.ribb...@pharmetheus.com
> 
> www.pharmetheus.com
> 
> 
> 
> Phone, Office:+46 (0)18 513 328
> 
> Uppsala Science Park, Dag Hammarskjölds väg 52B
> 
> SE-752 37 Uppsala, Sweden
> 
> 
> 
> This communication is confidential and is only intended for the use of the 
> individual or entity to which it is directed. It may contain information that 
> is privileged and exempt from disclosure under applicable law. If you are not 
> the intended recipient please notify us immediately. Please do not copy it or 
> disclose its contents to any other person.
> 
> 
> 
> 
> 
> On 02 Jun 2016, at 04:27, Abu Helwa, Ahmad Yousef Mohammad - abuay010 
>  wrote:
> 
>> Dear NMusers,
>>  
>> I am developing a PK model using log-transformed single-dose oral data. My 
>> question relates to using combined error model for log-transform data.
>>  
>> I have read few previous discussions on NMusers regarding this, which were 
>> really helpful, and I came across two suggested formulas (below) that I 
>> tested in my PK models.  Both formulas had similar model fits in terms of 
>> OFV (OFV using Formula 2 was one unit less than OFV using Formula1) with 
>> slightly changed PK parameter estimates. My issue with these formulas is 
>> that the model simulates very extreme concentrations (e.g. upon generating 
>> VPCs) at the early time points (when drug concentrations are low) and at 
>> later time points when the concentrations are troughs. These simulated 
>> extreme concentrations are not representative of the model but a result of 
>> the residual error model structure.
>>  
>> My questions:
>> 1.   Is there a way to solve this problem for the indicated formulas?
>> 2.   Are the two formulas below equally valid?
>> 3.   Is there an alternative formula that I can use which does not have 
>> this numerical problem?
>> 4.   Any reference paper that discusses this subject?
>>  
>> Here are the two formulas:
>> 1.   Formula 1: suggested by Mats Karlsson with fixing SIGMA to 1:
>> W=SQRT(THETA(16)**2+THETA(17)**2/EXP(IPRE)**2)
>>  
>> 2.   Formula 2: suggested by Leonid Gibiansky with fixing SIGMA to 1:
>> W = SQRT(THETA(16)+ (THETA(17)/EXP(IPRE))**2  )
>>  
>> The way I apply it in my model is this:
>>  
>> FLAG=0 ;TO AVOID ANY CALCULATIONS OF LOG (0)
>> IF (F.EQ.0) FLAG=1 
>> IPRE=LOG(F+FLAG)
>>  
>> W=SQRT(THETA(16)**2+THETA(17)**2/EXP(IPRE)**2) ;FORMULA 1
>>  
>> IRES=DV-IPRE
>> IWRES=IRES/W
>> Y=(1-FLAG)*IPRE + W*EPS(1)
>>  
>> $SIGMA
>> 1. FIX
>>  
>> Best regards,
>>  
>> Ahmad Abuhelwa
>> School of Pharmacy and Medical Sciences
>> University of South Australia- City East Campus
>> Adelaide, South Australia
>> Australia
>

Re: [NMusers] Additive plus proportional error model for log-transform data

2016-06-01 Thread Jakob Ribbing 
Hi Ahmad,

The two error models are equivalent (only that with Leonids suggested code, the 
additive-on-log-transformed error term (TH16) is estimated on variance scale, 
instead of standard deviation scale (approximate CV).
This inflated error rates for very low concentrations is what you get for 
additive+proportional on the log transformed scale, and I believe that has been 
discussed on nmusers previously as well, many years ago.
You could possibly use a cut-off for when lower IPRE should not lead to higher 
residual errors, but why not move to additive + proportional for the original 
concentration scale?

Also, this implementation may be unfortunate:
> Y=(1-FLAG)*IPRE + W*EPS(1)
Effectively, when concentration predictions are zero (FLAG=1), e.g. for 
pre-dose samples or before commence of absorption, then you set the 
concentration prediction to EXP(1)=3.14 concentration units.

Depending on what concentration scale you work on (i.e. if BLQ is much higher 
than this) it may be OK, but otherwise not.
Instead of applying a flag, just set IPRE to a negative value (low in relation 
to LOG(BLQ)), if you want to stay on the log-transformed scale.

I hope this helps to solve your problem.

Best regards

Jakob



Jakob Ribbing, Ph.D.

Senior Consultant, Pharmetheus AB



Cell/Mobile:+46 (0)70 514 33 77

jakob.ribb...@pharmetheus.com

www.pharmetheus.com



Phone, Office:  +46 (0)18 513 328

Uppsala Science Park, Dag Hammarskjölds väg 52B

SE-752 37 Uppsala, Sweden



This communication is confidential and is only intended for the use of the 
individual or entity to which it is directed. It may contain information that 
is privileged and exempt from disclosure under applicable law. If you are not 
the intended recipient please notify us immediately. Please do not copy it or 
disclose its contents to any other person.





On 02 Jun 2016, at 04:27, Abu Helwa, Ahmad Yousef Mohammad - abuay010 
 wrote:

> Dear NMusers,
>  
> I am developing a PK model using log-transformed single-dose oral data. My 
> question relates to using combined error model for log-transform data.
>  
> I have read few previous discussions on NMusers regarding this, which were 
> really helpful, and I came across two suggested formulas (below) that I 
> tested in my PK models.  Both formulas had similar model fits in terms of OFV 
> (OFV using Formula 2 was one unit less than OFV using Formula1) with slightly 
> changed PK parameter estimates. My issue with these formulas is that the 
> model simulates very extreme concentrations (e.g. upon generating VPCs) at 
> the early time points (when drug concentrations are low) and at later time 
> points when the concentrations are troughs. These simulated extreme 
> concentrations are not representative of the model but a result of the 
> residual error model structure.
>  
> My questions:
> 1.   Is there a way to solve this problem for the indicated formulas?
> 2.   Are the two formulas below equally valid?
> 3.   Is there an alternative formula that I can use which does not have 
> this numerical problem?
> 4.   Any reference paper that discusses this subject?
>  
> Here are the two formulas:
> 1.   Formula 1: suggested by Mats Karlsson with fixing SIGMA to 1:
> W=SQRT(THETA(16)**2+THETA(17)**2/EXP(IPRE)**2)
>  
> 2.   Formula 2: suggested by Leonid Gibiansky with fixing SIGMA to 1:
> W = SQRT(THETA(16)+ (THETA(17)/EXP(IPRE))**2  )
>  
> The way I apply it in my model is this:
>  
> FLAG=0 ;TO AVOID ANY CALCULATIONS OF LOG (0)
> IF (F.EQ.0) FLAG=1 
> IPRE=LOG(F+FLAG)
>  
> W=SQRT(THETA(16)**2+THETA(17)**2/EXP(IPRE)**2) ;FORMULA 1
>  
> IRES=DV-IPRE
> IWRES=IRES/W
> Y=(1-FLAG)*IPRE + W*EPS(1)
>  
> $SIGMA
> 1. FIX
>  
> Best regards,
>  
> Ahmad Abuhelwa
> School of Pharmacy and Medical Sciences
> University of South Australia- City East Campus
> Adelaide, South Australia
> Australia

[NMusers] Additive plus proportional error model for log-transform data

2016-06-01 Thread Abu Helwa, Ahmad Yousef Mohammad - abuay010 
Dear NMusers,

I am developing a PK model using log-transformed single-dose oral data. My 
question relates to using combined error model for log-transform data.

I have read few previous discussions on NMusers regarding this, which were 
really helpful, and I came across two suggested formulas (below) that I tested 
in my PK models.  Both formulas had similar model fits in terms of OFV (OFV 
using Formula 2 was one unit less than OFV using Formula1) with slightly 
changed PK parameter estimates. My issue with these formulas is that the model 
simulates very extreme concentrations (e.g. upon generating VPCs) at the early 
time points (when drug concentrations are low) and at later time points when 
the concentrations are troughs. These simulated extreme concentrations are not 
representative of the model but a result of the residual error model structure.

My questions:

1.   Is there a way to solve this problem for the indicated formulas?

2.   Are the two formulas below equally valid?

3.   Is there an alternative formula that I can use which does not have 
this numerical problem?

4.   Any reference paper that discusses this subject?

Here are the two formulas:

1.   Formula 1: suggested by Mats Karlsson with fixing SIGMA to 1:

W=SQRT(THETA(16)**2+THETA(17)**2/EXP(IPRE)**2)



2.   Formula 2: suggested by Leonid Gibiansky with fixing SIGMA to 1:

W = SQRT(THETA(16)+ (THETA(17)/EXP(IPRE))**2  )


The way I apply it in my model is this:

FLAG=0 ;TO AVOID ANY CALCULATIONS OF LOG (0)
IF (F.EQ.0) FLAG=1
IPRE=LOG(F+FLAG)

W=SQRT(THETA(16)**2+THETA(17)**2/EXP(IPRE)**2) ;FORMULA 1

IRES=DV-IPRE
IWRES=IRES/W
Y=(1-FLAG)*IPRE + W*EPS(1)

$SIGMA
1. FIX

Best regards,

Ahmad Abuhelwa
School of Pharmacy and Medical Sciences
University of South Australia- City East Campus
Adelaide, South Australia
Australia