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RE: PRED for BLQ-like observations

From: Pavel Belo <nonmem>
Date: Fri, 20 Nov 2015 17:15:35 -0500 (EST)

That is perfect!
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)).  The=
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 <>


From: owner-nmusers
On Behalf Of Nick Holford

Sent: Friday, November 20, 2015 12:39 PM

To: nmusers

Subject: Re: [NMusers] PRED for BLQ-like observations



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.


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




> 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

> [mailto:owner-nmusers
> <mailto:owner-nmusers

> *Sent:* Friday, November 20, 2015 11:47 AM

> *To:* nmusers

> *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

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 Fri Nov 20 2015 - 17:15:35 EST

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