Re: [NMusers] PRED for BLQ-like observations

From: Pavel Belo <nonmem_at_optonline.net>
Date: Thu, 26 Nov 2015 07:57:01 -0500 (EST)

It is a good point in general and I'll try to use it for simple cases of
BLQ values.  In this particular case, Y is a function of more than 1=
 
PHI, which requires a numerical method to get IPRED back (assuming in=
 
this case it is PRED). 


 
 
 On Sat, Nov 21, 2015 at 10:23 AM, Leonid Gibiansky wrote:
 
 > As we always do post-processing any way, one option is to use PRED=
 
provided by Nonmem to compute inverse cumulative distribution function
(qnorm in R, for example) and then restore PRED value
>
> IPRED = ...
> W = ...
> LLOQ = ...
> IF(BQL.EQ.1) Y=PHI((LLOQ-IPRED)/W)
>
> If you have PRED LLOQ and W in the Nonmem output file (that you read
> to the R data frame "data"), you can re-define
>
> if(BQL == 1) data$PRED = data$LLOQ - qnorm(data$PRED)*data$W # R=
 
> code
>
> (I have not tested it; use on your own risk :) )
>
> Leonid
>
>
> --------------------------------------
> Leonid Gibiansky, Ph.D.
> President, QuantPharm LLC
> web: www.quantpharm.com
> e-mail: LGibiansky at quantpharm.com
> tel: (301) 767 5566
>
>
>
> On 11/20/2015 5:15 PM, Pavel Belo wrote:
>> That is perfect!
>> P.
>> On Fri, Nov 20, 2015 at 04:04 PM, Bauer, Robert wrote:
>>
>> Unfortunately adding records to estimation slows down estimation
>> even with MDV=1 records. Please do a search on MDV=101 option in
>> nm730.pdf 1 (section Ignoring Non-Impact Records During
>> Estimation
>> (NM73)). These records will be used only on the $TABLE step.
>>
>> Robert J. Bauer, Ph.D.
>>
>> Vice President, Pharmacometrics R&D
>>
>> ICON Early Phase
>>
>> Office: (215) 616-6428
>>
>> Mobile: (925) 286-0769
>>
>> Robert.Bauer_at_iconplc.com
>> www.iconplc.com
>> *From:*owner-nmusers_at_globomaxnm.com
>> [mailto:owner-nmusers_at_globomaxnm.com] *On Behalf Of *Nick Holford
>> *Sent:* Friday, November 20, 2015 12:39 PM
>> *To:* nmusers
>> *Subject:* Re: [NMusers] PRED for BLQ-like observations
>>
>> Pavel,
>> Did you test the run time with double the records?
>> I would expect that the MDV=1 records would be largely ignored in=
 
>> the
>> estimation step and not contribute much to run time.
>> Nick
>>
>> On 21-Nov-15 08:59, Pavel Belo wrote:
>>> Thank you Bill,
>>> In my case it exactly doubles the number of records... The records
>>> are daily measures and the code is running slow enough. I'll
>> split the
>>> code into estimation part and one that that is redundant, but uses a
>>> larger file and creates an output. It will be something like
>>> $EST MAXEVALS=9999 SIG=3 NOABORT PRINT=1 SORT CONSTRAIN=5
>>> METHOD=SAEM NBURN=0 NITER=0 POSTHOC INTERACTION
>>> LAPLACIAN GRD=TG(1-7):TS(8-9) CTYPE=3 CINTERVAL=10
>>> I guess the best future way is modify something in NONMEM so
>> there is
>>> an option to provide only PRED in the PRED column (version 7.4?).
>>> Thanks!
>>> Pavel
>>> On Fri, Nov 20, 2015 at 01:06 PM, Denney, William S. wrote:
>>>
>>> Hi Pavel,
>>>
>>> The easiest way that I know is to generate your data file with one
>>> set of rows for estimation with M3 and another row just above or
>>> below with MDV=1. NONMEM will then provide PRED and IPRED in the
>>> rows with MDV=1.
>>>
>>> Thanks,
>>>
>>> Bill
>>>
>>> *From:*owner-nmusers_at_globomaxnm.com
>>> [mailto:owner-nmusers_at_globomaxnm.com] *On Behalf Of *Pavel Belo
>>> *Sent:* Friday, November 20, 2015 11:47 AM
>>> *To:* nmusers_at_globomaxnm.com *Subject:* [NMusers] PRED for BLQ-like
>>> observations
>>>
>>> Hello The NONMEM Users,
>>>
>>> When we use M3-like approach, the outputs has PRED for non-missing
>>> observations and something else for BLQ (is that PRED=CUMD?). As
>>> in the diagnostic figures PRED for BLQs looks like noise, I remove
>>> them. It is not always perfect, but OK in for most frequent cases.
>>>
>>> When we use count data such as a scale with few possible values
>>> (for example, 0, 1, 2, 3, 4, 5), it makes more sense to use PHI
>>> function (home-made likelihood) for all observations rather than
>>> to treat the count as a continuous variable an apply M3-like
>>> approach to 1 and 5 while only (as we know, they are like LLOQ and
>>> ULOQ). In this case, all PRED values look like noise. A hard way
>>> to replace the noise with PRED value is to simulate PRED for each
>>> point and merge them with the DV and IPRED data. Is there an easy
>>> way?
>>>
>>> (The model runs well and better than when the count is treated as
>>> a continuous variable.)
>>>
>>> Thanks!
>>>
>>> Pavel
>>>
>>
>> --
>> 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
>> email: n.holford_at_auckland.ac.nz
>> http://holford.fmhs.auckland.ac.nz/
>>
>> Holford SD, Allegaert K, Anderson BJ, Kukanich B, Sousa AB,
>> Steinman
>> A, Pypendop, B., Mehvar, R., Giorgi, M., Holford,N.H.G.
>> Parent-metabolite pharmacokinetic models - tests of assumptions
>> and
>> predictions. Journal of Pharmacology & Clinical Toxicology.
>> 2014;2(2):1023-34.
>> Holford N. Clinical pharmacology = disease progression + drug
>> action. Br J Clin Pharmacol. 2015;79(1):18-27.
>>
>>
>>
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Received on Thu Nov 26 2015 - 07:57:01 EST

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