Dear Xinting,
I think you should try to change the nonmem control stream name for the
dependent variable, from DV=LNDV (which is what I assume you have right now on
$INPUT?) to just read DV.
The name for the DV column in the datafile can remain LNDV, so this is just a
matter of changing on
Hi Pavel,
I agree with you it is not uncommon to have AUC drive efficacy or safety
endpoints.
However, you seem to have the impression this is commonly done using cumulative
AUC and I can assure you that is rarely the case.
I have only seen that for safety endpoints where it has been justified
Hi Nele,
I believe Matt's point was more to the situation where any remaining
correlation between CL and V random components can not be accounted for by
covariates, so that both eta on F and block2 on CL and V is used?
If eta on F and covariates takes care of the correlation between CL and V:
to the thread before sending a reply!
Jakob
-Original Message-
From: Ribbing, Jakob
Sent: 26 November 2013 10:46
To: Mueller-Plock, Nele; Leonid Gibiansky; 'nmusers'
Cc: Ribbing, Jakob
Subject: RE: [NMusers] Getting rid of correlation issues between CL and volume
parameters
Hi Nele,
I
Hi Pavel,
Since you say that IPRED shows two Cmax this could not be residual error.
Like Leonid I would believe this is rather an error in setup (control stream or
dataset) rather than an error with nonmem.
However, since you are using an ADVAN13 model, I suppose if the dose is split
into
Dear Jules,
You are correct in pointing out where the problem lies.
However, the covariance matrix is not fine since it is at the boundary just as
much as the translation into correlation.
Correlation is calculated by this equation:
Cor(eta1,eta2)=OM1,2 /(sqrt(OM1,1)*sqrt(OM2,2))
Where:
OM1,1 and
Dear all,
Since Monday I have made three attempts to post a reply on the thread question
in Box-Cox Transformations in K-PD model, initiated by Kehua.
None of these attempts have been successful, at the same time I have noticed
that other topics have successfully reached out via the
From: Ribbing, Jakob
Sent: 12 August 2013 23:45
To: 'kehua wu'; nmusers
Cc: Ribbing, Jakob
Subject: RE: [NMusers] Fwd: question in Box-Cox Transformations in K-PD model
Hi Kehua,
When you say that you did not get the estimate of TH2 in the output file, but
you got the initial estimate. Did you
, Jakob; nmusers@globomaxnm.com; robert.ba...@iconplc.com
Subject: RE: [NMusers] Unable to post to nmusers
Have a look on this old post...
From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On
Behalf Of Ribbing, Jakob
Sent: den 16 augusti 2013 14:37
To: nmusers@globomaxnm.com
Dear Felipe,
The distribution obtained from the (nonparametric) bootstrap represents
uncertainty in the population parameters, and the histogram for V1 should not
be interpreted as a distribution of individual parameter values. There are
issues with relying on the nonparametric distribution
Resending, since my posting from this morning (below) has not yet appeared on
nmusers.
Apologies for any duplicate postings!
From: Ribbing, Jakob
Sent: 18 February 2013 09:59
To: Kågedal, Matts; nmusers@globomaxnm.com
Cc: Ribbing, Jakob
Subject: RE: Simulation settgin in the precence
Hi Kehua,
If I understand you correctly you screened thousands of genotypes to find those
that appeared to be the (60 most) promising predictors?
Were the asthma patients in your nonmem analysis part of the material you used
for GWAS screen, or is the nonmem analysis based on external data from
Dear Palang and Martin,
For the published analysis; do you have any information on the covariates that
you would like to investigate? (mean and sd or range). Another factor weighting
in the approach you take may be what functional form(s) you consider for
continuous covariates (e.g. Linear vs.
All,
I think Leonid and Neil have pointed out two plausible explanations, I just
wanted to highlight that these are two separate issues:
* If you have an additive component in your error model a graph on log
scale would appear to widen at the end. This is fine. In this particular case
All,
This first part is more to clarify and I do not believe this is in
disagreement with what has been said before. The last paragraph is a
question.
The two examples I mentioned regarding boundary conditions are regarding
variance parameters. The second of these, however, is with regards to a
Resending and apologizing for any duplicate messages!
-Original Message-
From: Ribbing, Jakob
Sent: 11 July 2011 10:13
To: nmusers
Subject: RE: [NMusers] Confidence intervals of PsN bootstrap output
All,
This first part is more to clarify and I do not believe this is in
disagreement
[mailto:kajsa.harl...@farmbio.uu.se]
Sent: 23 June 2011 11:52
To: General Discussion about PsN.
Subject: Re: [Psn-general] bootstrap problem
Thank you for the error report. This will be fixed in the next release.
Best regards,
Kajsa
-Original Message-
From: Ribbing, Jakob
Dear all,
Previous attempts to send this e-mail appear to have been unsuccessful.
I am resending with further reduction in text and apologize in case of
any duplicate or triplicate postings.
Jakob
From: Ribbing, Jakob
Sent: 08 July 2011 16:31
It seems my previous attempt to post this was unsuccessful (either
because of the graphs included or because 70 kb was too much?)
I am resending without graphs and apologize in case of any duplicate
postings!
From: Ribbing, Jakob
Sent: 08 July 2011 16:31
Dear Liu,
In nonmem you can not define differential equations through a loop; each
has to be explicitly written out.
However, I notice that you have the same rate of transit among all of
your (transit) compartments.
Therefore, an analytical solution would be the most efficient way of
solving
Dear all,
A single control stream with $SIM followed by $EST could possibly do the trick
(similar to what Luann suggests below, using two control streams). However,
before we air additional suggestions on how we may or may not achieve
random-number generation during estimation; maybe it is
Dear all,
Dropping in a little late in the game all I can say is this:
Shame on all you great minds for reinventing your own wisdom :)
Most of the content in the current thread has already been discussed in
an earlier thread:
Dear Ye hong bo,
If I understand you correctly no single sample has been assayed with multiple
assay methods? It may be that the assay method only makes a small contribution
to the overall residual, but if you have enough information on the three SIGMAs
you may keep it as three separate
Dirk,
I think the approach is influenced by what this lab value represents. If it is
a biomarker/endpoint that is influenced by drug treatment then the best
approach is to include this in your PK-PD model as a dependent variable. If you
treat this as a traditional covariate it should not be
Andreas,
The code snippet you picked out is not overparameterized, since the
assumption is made that the variance of eta 5 and 6 are the same:
$OMEGA BLOCK(1) 0.05
$OMEGA BLOCK(1) SAME
This first equation that you suggest is this:
IOV2=0
IF (DESC.EQ.2) IOV2=1
ETCL = ETA(1)+IOV2*ETA(5)
Hi Pyry,
Yes, when calculating shrinkage or looking at eta-diagnostic plots it is often
better to exclude etas from subjects that has no information on that parameter
at all. For a PK model we would not include subjects that were only
administered placebo (if PK is exogenous compound). In
would more trust diagnostics after
exclusion.
Jakob
From: Eleveld, DJ [mailto:d.j.elev...@anest.umcg.nl]
Sent: 21 August 2009 13:57
To: Ribbing, Jakob; Pyry Välitalo; nmusers@globomaxnm.com
Subject: RE: [NMusers] Calculating shrinkage when some etas are zero
Dear Khaled,
You could for example report this as Including covariate X in the
model, the estimate of random (unexplained) between-subject variability
in parameter Y reduced from 41.8 %CV to 40.5 %CV.
Reporting % explained variability may lead to confusion on if this is in
percent or
Hi Ethan,
If OMEGA(?) for KA is drastically reduced when including the sparse
data, then something is wrong with your model and in this case it is not
the estimation method or assumption on distribution of individual
parameter). Eta-shrinkage would not drastically reduce the estimate of
OMEGA,
...@yahoo.com]
Sent: 17 June 2009 22:05
To: Ribbing, Jakob; Jurgen Bulitta; nmusers@globomaxnm.com
Cc: Roger Jelliffe; Neely, Michael
Subject: Re: [NMusers] estimating Ka from dataset combining rich sample
study and sparse sampling study
Hi Jakob,
sparse data came from MD study. and IIV on CL
Andreas,
PsN has functionality for automatically applying the CONT-data-item
approach mentioned by Mark and Nick.
To run your model in nonmem with more than 20 input variables, you would
simply type:
execute --wrap_data run1.mod
In a subdirectory PsN will create a new dataset and model file
Ethan,
Sebastian is right that a non-parametric bootstrap may be suitable for
determining the uncertainty in the population parameters. However, I got
the impression that you wanted to investigate possible study designs on
how informative they are for a future model-based analysis? If you
Leonid,
As I understand the linear model you suggested it can be simplified* to
this structure:
THETA(1)*((WT/70)^(3/4)+THETA(2)*CRCL)
I call this additive, because the two covariates affect TVCL in an
absolute sense, without interaction. My main message was that I find
this model appealing,
Pete,
Is the drug cleared almost completely thru renal elimination?
Otherwise, maybe a slope intercept model for CL as a function of CRCL?
TVCL=THETA(X)*(WT/70)**0.75+THETA(Y)*CRCL
The intercept is nonrenal CL according to the allometric model and the
slope according to CRCL. This
Leonid,
I usually prefer multiplicative parameterisation as well, since it is
easier to set boundaries (which is not necessary for power models, but
for multiplicative-linear models). However, boundaries on the additive
covariate models can still be set indirectly, using EXIT statements (not
as
Correction, I meant WT 50 and 75 in the example below:
75^0.75/(50^0.75)=1.36
-Original Message-
From: Ribbing, Jakob
Sent: 13 January 2009 00:50
To: nmusers@globomaxnm.com; 'Leonid Gibiansky'; Bonate, Peter
Subject: RE: [NMusers] CrcL or Cr in pediatric model
Leonid,
I usually prefer
. Annu Rev Pharmacol Toxicol 48:
303-32.
Ribbing, Jakob wrote:
Correction, I meant WT 50 and 75 in the example below:
75^0.75/(50^0.75)=1.36
-Original Message-
From: Ribbing, Jakob
Sent: 13 January 2009 00:50
To: nmusers@globomaxnm.com; 'Leonid Gibiansky'; Bonate, Peter
this and can you explain how it arises? In the worst case of
shrinkage then bias is impossible because all ETAs must be zero. So why
does it occur with non-zero shrinkage?
Nick
Ribbing, Jakob wrote:
Dear all,
First of all, I am not sure that there is any assumption of etas
having
a normal
Dear Huali,
The best is to derive one model for all data. If you are pressed with
time it may be sufficient to describe the data in the dose range which
is clinically relevant (if known). Possibly, in this dose range there is
no nonlinerarity. However, splitting the subjects based on the
Dear Li,
You are right in thinking that your baseline better not be treated as an
ordinary covariate (where we pretend that the covariate values are
measured without error). Unless you want to be very restrictive in how
to use the model (e.g. change from baseline in a study with similar
design
Hi Bernad,
Regarding differences between nm5 and 6, you can try to run both models
with different initial estimates, to see if the two minima are stable
within a nonmem version. Which minima yield the lowest OFV? Do you have
information in your data to describe a two-compartment model? What is
Hi all,
I think that Paul stumbled on a rather important issue. The SE of the residual
error may not be of primary interest, but the same as discussed under this
thread also applies to the standard error of omega. (I changed the name of the
subject since this thread now is about omega)
I
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