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Re: PRED for BLQ-like observations

From: Nick Holford <n.holford>
Date: Sat, 21 Nov 2015 09:38:56 +1300

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
> *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.
Received on Fri Nov 20 2015 - 15:38:56 EST

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