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

From: Leonid Gibiansky <lgibiansky_at_quantpharm.com>
Date: Fri, 11 Sep 2015 16:01:23 -0400

I think that the procedure that you suggested (simulation - correlation
- popPK model) may not be reliable in general case as it assumes (or
will result in the data) that the profiles of individual subjects are
ordered at all time points. It correspond to some special (but explicit)
assumptions about random effect structure. I would rather use explicit
assumptions.
Leonid


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



On 9/11/2015 1:38 PM, Penny Zhu wrote:
> Dear Dr Gibiansky
> Thank you very much for the suggestion. I largely agree with you that it seems to be an trial and error thing to make the variability match the model prediction if we have a strong assumption about the model structure.
>
> I was also wondering whether it is possible to simulate individual patient data at each timepoint based on the mean, steandard deviation, and an using an assumption that within patients (especially in adjacent timepionts) the Pk concentrations are more correlated compared to between patients. Then use these simulated data to fit the population PK model.
>
> Best regards.
>
> Penny Zhu
>
>
>
>> -----Original Message-----
>> From: Leonid Gibiansky [mailto:lgibiansky_at_quantpharm.com]
>>
>> Sent: Thursday, September 10, 2015 5:10 PM
>> To: Zhu, Penny; nmusers_at_globomaxnm.com
>> Subject: Re: FW: [NMusers] Question of fitting population PK
>> model using summary statistics of data instead of raw data
>>
>> It is likely impossible without strong assumptions. I would
>> first fit the population model (fixed effects only) and then
>> start to simulate with different assumption trying to match
>> observed SD or CV for peaks and troughs. You may need to
>> assume the structure and the magnitude of the error model
>> and the structure of the IIV model (ETAs on CL, or V, or
>> both equal, etc.). You 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 standard deviation
>> data.
>>>
>>> My intention is to establish a population PK/PD model
>> with appropriate estimation of intersubject variability
>> based on the mean and standard deviation 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 Rekić, 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
>>>
>>
>
Received on Fri Sep 11 2015 - 16:01:23 EDT

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