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Re: [NMusers] Question of fitting population PK model using summary statistics of data instead of raw data

From: Michael Fossler <mfossler_at_trevenainc.com>
Date: Fri, 11 Sep 2015 01:16:25 +0000

I would argue that it is impossible. With mean data and SD's at each time-p=
oint, it is impossible to separate between and with-subject variability.
However, a model with "strong assumptions", as Leonid has aptly put it, may=
 still be very useful. It should not be too difficult to come up with reaso=
nable BSV estimates, and the residual error estimates could probably be tak=
en from the assay information, once you have fit the mean curve. You should=
 not expect too much from such a model, but it may be helpful in simulating=
 some future studies.

Sent from my iPhone

> On Sep 10, 2015, at 6:00 PM, Leonid Gibiansky <lgibiansky_at_quantpharm.com>=
 wrote:
>
> It is likely impossible without strong assumptions. I would first fit the=
 population model (fixed effects only) and then start to simulate with diff=
erent assumption trying to match observed SD or CV for peaks and troughs. Y=
ou may need to assume the structure and the magnitude of the error model an=
d the structure of the IIV model (ETAs on CL, or V, or both equal, etc.). Y=
ou may get some rough idea about the magnitude of the IIV but you may need =
strong assumptions about the residual and IIV model.
> Leonid
>
>
> --------------------------------------
> Leonid Gibiansky, Ph.D.
> President, QuantPharm LLC
> web: www.quantpharm.com
> e-mail: LGibiansky at quantpharm.com
> tel: (301) 767 5566
>
>
>
>> On 9/10/2015 2:06 PM, Penny Zhu wrote:
>> Dear Dinko
>> Thank you for the suggestion. It seems this NAD approach only uses the =
mean data and does not estimate inter-subject variability using the standar=
d deviation data.
>>
>> My intention is to establish a population PK/PD model with appropriate e=
stimation of intersubject variability based on the mean and standard deviat=
ion data at each timepoint.
>>
>> A major assumption is that we have good knowledge of the base structure =
of the model (e.g. biexponential), and won't run the risk mistaking 2 mono =
exponential models for a biexponential model
>>
>> Your help and discussions will be very much appreciated.
>>
>> Penny
>>
>>
>> -----Original Message-----
>> From: Rekic, Dinko [mailto:Dinko.Rekic_at_fda.hhs.gov]
>> Sent: Thursday, September 10, 2015 10:41 AM
>> To: Zhu, Penny
>> Subject: RE: [NMusers] Question of fitting population PK
>> model using summary statistics of data instead of raw data
>>
>> See the link and text below.
>>
>> http://accp1.org/pharmacometrics/theory_popmeth.htm#npd
>>
>>
>> Naive averaged data approach (NAD)
>>
>> A model without BSV and BOV is fitted to the
>> mean data from all individuals.
>>
>> Features
>>
>> -Specialized software not
>> necessary.
>>
>> Disadvantages
>>
>> -Does not distinguish between
>> BSV and WSV.
>>
>> -Inappropriate means lead to
>> biased parameter estimates.
>>
>> -May produce model distortion
>> i.e., 2 mono exponential equations averaged together can
>> yield a biexponential.
>>
>> -Covariate modeling cannot be
>> performed.
>>
>> Kind regards
>> Dinko
>> _________________________________
>> Dinko RekiŠ, Ph.D., MSc(Pharm)
>> Pharmacometrics reviewer
>> Division of Pharmacometrics
>> Office of Clinical Pharmacology
>> Office of Translational Science
>> Center for Drug Evaluation and Research
>> U.S. Food and Drug Administration
>> 10903 New Hampshire Ave
>> Silver Spring, MD 20993
>> WO Bldg 51, Rm 3122
>> Office phone: (8)240 402-3785
>>
>> "The contents of this message are mine personally and do not
>> necessarily reflect any position of the Government or the
>> Food and Drug Administration."
>>
>> -----Original Message-----
>> From: owner-nmusers_at_globomaxnm.com
>> [mailto:owner-nmusers_at_globomaxnm.com]
>> On Behalf Of Penny Zhu
>> Sent: Thursday, September 10, 2015 9:49 AM
>> To: nmusers_at_globomaxnm.com
>> Subject: [NMusers] Question of fitting population PK model
>> using summary statistics of data instead of raw data
>>
>> Dear all
>> Assuming the population PK or PD data are log-normally (or
>> normally) distributed, if you have the mean and standard
>> deviation of a readout at each timepoint but do not have the
>> actual raw data (assuming all pateints are with the same
>> dosing regimen, etc), is it possible to establish a
>> well fitted population PK or PD model? How would one
>> get about doing it?
>>
>> Your help is very much appreciated
>>
>> Penny
>>

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Received on Thu Sep 10 2015 - 21:16:25 EDT

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