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Re: [NMusers] PRED for BLQ-like observations

From: Fisher Dennis <fisher_at_PLessThan.com>
Date: Fri, 20 Nov 2015 13:56:27 -0800

Bill

I suspect that Bob will be able to answer this better. But, it is =
probably just create something “parallel” to 101.

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 <http://www.plessthan.com/>



> On Nov 20, 2015, at 1:51 PM, Denney, William S. =
<William.S.Denney_at_pfizer.com> wrote:
>
> Hi Bob and Dennis,
>
> I was unaware of these values, thanks for the pointer. What is a use =
case for MDV=100? The only case I can think of is if you have a =
measurement that you don't believe to be accurate, but then it should be =
removed and/or set to actually missing before NONMEM.
>
> Thanks,
>
> Bill
>
> On Nov 20, 2015, at 16:42, "Fisher Dennis" <fisher_at_plessthan.com =
<mailto:fisher_at_plessthan.com>> wrote:
>
> 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 =
<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.PLessThan.com&d=
=CwMFAg&c=UE1eNsedaKncO0Yl_u8bfw&r=4WqjVFXRfAkMXd6y3wiAtxtNlICJwFMio=
goD6jkpUkg&m=WYu-CQioIB0i7YgHqkz6PNMt7uCea2R_jfzrL98PYfw&s=zc442lHm9Sn=
0NQGqUpk8TZgvysYTMDYaSmndj8HXFhY&e=>
>
>
>
>> On Nov 20, 2015, at 12:38 PM, Nick Holford <n.holford_at_auckland.ac.nz =
<mailto:n.holford_at_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-nmusers_at_globomaxnm.com =
<mailto:owner-nmusers_at_globomaxnm.com>
>>> [mailto:owner-nmusers_at_globomaxnm.com =
<mailto:owner-nmusers_at_globomaxnm.com>] *On Behalf Of *Pavel Belo
>>> *Sent:* Friday, November 20, 2015 11:47 AM
>>> *To:* nmusers_at_globomaxnm.com <mailto:nmusers_at_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.holford_at_auckland.ac.nz <mailto:n.holford_at_auckland.ac.nz>
>> http://holford.fmhs.auckland.ac.nz/ =
<https://urldefense.proofpoint.com/v2/url?u=http-3A__holford.fmhs.auckla=
nd.ac.nz_&d=CwMFAg&c=UE1eNsedaKncO0Yl_u8bfw&r=4WqjVFXRfAkMXd6y3wiAtx=
tNlICJwFMiogoD6jkpUkg&m=WYu-CQioIB0i7YgHqkz6PNMt7uCea2R_jfzrL98PYfw&s==
mXOHPHTRH3KFb_dSGnMz_dQtDkhhBtasaU3R5_x-Ip4&e=>
>>
>> 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 - 16:56:27 EST

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