Even better, take advantage of this (from NMHELP):
Values of MDV are:
0 The DV data item is an observed value, i.e., DV is not miss-
ing.
1 The DV data item is not regarded an observed value, i.e., DV
is missing. The DV data item is ignored. |
100 Same as MDV=0, but this record is ignored during Estimation |
and Covariance Steps. During other steps, MDV will changed |
to 0. |
101 Same as MDV=1, but this record is ignored during Estimation |
and Covariance Steps. During other steps, MDV will changed |
to 1. |
Reserved variables MDVI1, MDVI2, MDVI3 can be used to over- |
ride values of MDV>100. These variables are defined in |
include file nonmem_reserved_general.
Dennis
Dennis Fisher MD
P < (The "P Less Than" Company)
Phone: 1-866-PLessThan (1-866-753-7784)
Fax: 1-866-PLessThan (1-866-753-7784)
www.PLessThan.com
> On Nov 20, 2015, at 12:38 PM, Nick Holford <n.holf...@auckland.ac.nz> wrote:
>
> 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.
>
