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

From: Jakob Ribbing <jakob.ribbing_at_pharmetheus.com>
Date: Thu, 2 Jun 2016 06:56:37 +0200

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.Ribbing_at_Pharmetheus.com

www.pharmetheus.com



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

Uppsala Science Park, Dag Hammarskjölds väg 52B

SE-752 37 Uppsala, Sweden



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On 02 Jun 2016, at 06:31, Jakob Ribbing <jakob.ribbing_at_pharmetheus.com> =
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.Ribbing_at_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 =
<ahmad.abuhelwa_at_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=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
>


Received on Thu Jun 02 2016 - 00:56:37 EDT

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