Re: PRED for BLQ-like observations
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
Quoted reply history
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
>
> [email protected] <mailto:[email protected]>
>
> www.iconplc.com http://www.iconplc.com
>
> *From:*[email protected]
> [mailto:[email protected]] *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:*[email protected]
> > [mailto:[email protected]] *On Behalf Of *Pavel Belo
> > *Sent:* Friday, November 20, 2015 11:47 AM
> > *To:* [email protected] <mailto:[email protected]>
> > *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: [email protected] <mailto:[email protected]>
> 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.
>
>