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-nmus...@globomaxnm.com
[mailto:owner-nmus...@globomaxnm.com] *On Behalf Of *Pavel Belo
*Sent:* Friday, November 20, 2015 11:47 AM
*To:* nmusers@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.holf...@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.
