Dear expert users,
I am working on a dataset where subjects were sampled at different visits at
random. I have developed a model for the data but not sure how to do a VPC as
they do not have the same sampling scheme. I appreciate some guidance in this.
Regards,
Ayyappa
VPC for non-uniform sampling
Dear Ayyappa,
You may consider using "Standardized Visual Predictive Check" published in the
following links:
http://jcp.sagepub.com/content/52/1/39
http://www.page-meeting.org/pdf_assets/4050-Standardized%20Visual%20Predictive%20Check%20in%20Model%20Evaluation%20-%20PAGE2009%20submit.pdf
Best regards,
Jean
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Ayyappa Chaturvedula
Sent: Tuesday, January 10, 2012 5:11 PM
To: [email protected]
Subject: [NMusers] VPC for non-uniform sampling
Dear expert users,
I am working on a dataset where subjects were sampled at different visits at
random. I have developed a model for the data but not sure how to do a VPC as
they do not have the same sampling scheme. I appreciate some guidance in this.
Regards,
Ayyappa
This electronic transmission may contain confidential and/or proprietary
information and is intended to be for the use of the individual or entity named
above. If you are not the intended recipient, be aware that any disclosure,
copying, distribution or use of the contents of this electronic transmission is
prohibited. If you have received this electronic transmission in error, please
destroy it and immediately notify us of the error. Thank you.
you could try creating uniformly distributed time points in a new data set
covering the whole time range in your observed data set and repeat the
simulation for 100-200 times for 100 subjects or whatever number you think
is reasonable for your study population size. New data set can be easily
created in R using few lines of codes.
Best
Jeet
Quoted reply history
On Tue, Jan 10, 2012 at 4:11 PM, Ayyappa Chaturvedula <
[email protected]> wrote:
> Dear expert users,****
>
> ** **
>
> I am working on a dataset where subjects were sampled at different visits
> at random. I have developed a model for the data but not sure how to do a
> VPC as they do not have the same sampling scheme. I appreciate some
> guidance in this. ****
>
>
> Regards,****
>
> Ayyappa****
>
--
Indrajeet Singh,PhD
Sr. Clinical Pharmacokineticist
Abbott Labs, North Chicago, IL
Simulating the data is only part of a VPC. The other part is describing the distribution of the actual observations. If data is collected honestly with actual sampling times then of observation times will be different for every subject whether or not the protocol also had some random element.
A solution to this is to bin the observed (and simulated) values around some suitable times e.g. using nominal protocol times or with more complex algorithms (see Lavielle et al. 2011). Then the distribution of observations and simulations can be compared at each of those times.
Lavielle M, Bleakley K. Automatic data binning for improved visual diagnosis of pharmacometric models. J Pharmacokinet Pharmacodyn. 2011;38(6):861-71.
Quoted reply history
On 11/01/2012 11:44 a.m., indrajeet singh wrote:
> you could try creating uniformly distributed time points in a new data set covering the whole time range in your observed data set and repeat the simulation for 100-200 times for 100 subjects or whatever number you think is reasonable for your study population size. New data set can be easily created in R using few lines of codes.
>
> Best
> Jeet
>
> On Tue, Jan 10, 2012 at 4:11 PM, Ayyappa Chaturvedula < [email protected] < mailto: [email protected] >> wrote:
>
> Dear expert users,
>
> I am working on a dataset where subjects were sampled at different
> visits at random. I have developed a model for the data but not
> sure how to do a VPC as they do not have the same sampling
> scheme. I appreciate some guidance in this.
>
> Regards,
>
> Ayyappa
>
> --
> Indrajeet Singh,PhD
> Sr. Clinical Pharmacokineticist
> Abbott Labs, North Chicago, IL
--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology& Clinical Pharmacology, Bldg 505 Room 202D
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53
email: [email protected]
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
It's quite interesting to see the discussions. As pointed out, there have
been a number of publications regarding better ways to do VPC. A follow up
question would be if these methods will be integrated into the PsN, which
is widely used to carry out standard VPC.
Thanks,
Norman
Quoted reply history
On Tue, Jan 10, 2012 at 6:09 PM, Nick Holford <[email protected]>wrote:
> Simulating the data is only part of a VPC. The other part is describing
> the distribution of the actual observations. If data is collected honestly
> with actual sampling times then of observation times will be different for
> every subject whether or not the protocol also had some random element.
>
> A solution to this is to bin the observed (and simulated) values around
> some suitable times e.g. using nominal protocol times or with more complex
> algorithms (see Lavielle et al. 2011). Then the distribution of
> observations and simulations can be compared at each of those times.
>
> Lavielle M, Bleakley K. Automatic data binning for improved visual
> diagnosis of pharmacometric models. J Pharmacokinet Pharmacodyn.
> 2011;38(6):861-71.
>
>
>
> On 11/01/2012 11:44 a.m., indrajeet singh wrote:
>
>> you could try creating uniformly distributed time points in a new data
>> set covering the whole time range in your observed data set and repeat the
>> simulation for 100-200 times for 100 subjects or whatever number you think
>> is reasonable for your study population size. New data set can be easily
>> created in R using few lines of codes.
>>
>> Best
>> Jeet
>>
>> On Tue, Jan 10, 2012 at 4:11 PM, Ayyappa Chaturvedula <
>> [email protected]
>> <mailto:chaturvedula_a@mercer.**edu<[email protected]>>>
>> wrote:
>>
>> Dear expert users,
>>
>> I am working on a dataset where subjects were sampled at different
>> visits at random. I have developed a model for the data but not
>> sure how to do a VPC as they do not have the same sampling
>> scheme. I appreciate some guidance in this.
>>
>>
>> Regards,
>>
>> Ayyappa
>>
>>
>>
>>
>> --
>> Indrajeet Singh,PhD
>> Sr. Clinical Pharmacokineticist
>> Abbott Labs, North Chicago, IL
>>
>>
> --
> Nick Holford, Professor Clinical Pharmacology
> Dept Pharmacology& Clinical Pharmacology, Bldg 505 Room 202D
> University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
> tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53
> email: [email protected]
> http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
>
>
Dear Norman, Ayyappa and NMusers,
The situation described by Ayyappa does not necessary call for any out of
the ordinary adjustments of a VPC. Especially so since it is stated that
observations are made at random. If the design in any way was conditioned on
the previous observations on the other hand the situation would be
different. In the case when the design is conditioned on the observations
e.g. TDM studies we have recently demonstrated that prediction corrected
VPCs (pcVPCs) are a better diagnostic than traditional VPCs [1]. The pcVPC
and pvcVPC (prediction and variability corrected VPC) have also been
demonstrated to have advantages in the presence of other influential
independent variables than the one depicted on the x-axis of the VPC
(typically time). It can be worthwhile to once again point out that a VPC
does not necessarily have to have time as the x-variable and DV as the
y-variable. For example in the Ayyappa example time-after-dose might be an
informative alternative as an x-variable and in some cases change from
baseline can be an informative y-variable.
Regarding the questions from Norman Z: Prediction and variability correction
are new functionalities that is included in released versions of PsN. PsN
also includes a lot of options for binning and further development in this
field is on its way. An atomized binning algorithm was recently published by
Lavielle et.al. [2] and implemented in MONOLIX. Atomized binning procedures
will also be included in future PsN releases. Other rather recent
functionalities in PsN with regards to VPCs is the ability to do VPCs for
Kaplan-Meier plots (time-to-event), VPCs functionality for categorical data
and handling of censored observations (BQL, dropout etc). As previously
discussed at PAGE and NMusers Standardized Visual Predictive Check is the
same as NPDEs previously developed by France Mentré.
[1] Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. Prediction-Corrected
Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models. AAPS
J. 2011 Feb 8.
[2] Lavielle M, Bleakley K. Automatic data binning for improved visual
diagnosis of pharmacometric models. J Pharmacokinet Pharmacodyn.
2011;38(6):861-71.
More about the PsN VPC functionalities can be read in this document:
http://psn.sourceforge.net/pdfdocs/npc_vpc_userguide.pdf
Kind regards,
Martin Bergstrand, PhD
Pharmacometrics Research Group
Dept of Pharmaceutical Biosciences
Uppsala University
Sweden
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Norman Z
Sent: Wednesday, January 11, 2012 2:30 AM
To: nmusers
Subject: Re: [NMusers] VPC for non-uniform sampling
It's quite interesting to see the discussions. As pointed out, there have
been a number of publications regarding better ways to do VPC. A follow up
question would be if these methods will be integrated into the PsN, which is
widely used to carry out standard VPC.
Thanks,
Norman
On Tue, Jan 10, 2012 at 6:09 PM, Nick Holford <[email protected]>
wrote:
Simulating the data is only part of a VPC. The other part is describing the
distribution of the actual observations. If data is collected honestly with
actual sampling times then of observation times will be different for every
subject whether or not the protocol also had some random element.
A solution to this is to bin the observed (and simulated) values around some
suitable times e.g. using nominal protocol times or with more complex
algorithms (see Lavielle et al. 2011). Then the distribution of observations
and simulations can be compared at each of those times.
Lavielle M, Bleakley K. Automatic data binning for improved visual diagnosis
of pharmacometric models. J Pharmacokinet Pharmacodyn. 2011;38(6):861-71.
On Tue, Jan 10, 2012 at 4:11 PM, Ayyappa Chaturvedula
<[email protected] <mailto:[email protected]>> wrote:
Dear expert users,
I am working on a dataset where subjects were sampled at different
visits at random. I have developed a model for the data but not
sure how to do a VPC as they do not have the same sampling
scheme. I appreciate some guidance in this.
Regards,
Ayyappa
--
Indrajeet Singh,PhD
Sr. Clinical Pharmacokineticist
Abbott Labs, North Chicago, IL
Dear Ayappa,
"Standardized Visual Predictive Check" are in fact prediction discrepancies
which were developed by Mentré et al., with a decorrelated version called
normalised prediction distribution errors (npde) in Brendel et al. npde are
available in Nonmem or Monolix. There is also a library in R to help compute
both pd and npde (the library is called npde).
Here are a few links to the original publications.
Prediction discrepancies and their evaluation:
Prediction discrepancies for the evaluation of nonlinear mixed-effects models.
Mentré F, Escolano S.
J Pharmacokinet Pharmacodyn. 2006 Jun;33(3):345-67. Epub 2005 Nov 13.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1989778/
The development of npde:
Metrics for external model evaluation with an application to the population
pharmacokinetics of gliclazide.
Brendel K, Comets E, Laffont C, Laveille C, Mentré F.
Pharm Res. 2006 Sep;23(9):2036-49. Epub 2006 Aug 12.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2124466/
An evaluation of the npde:
J Pharmacokinet Pharmacodyn. 2010 Feb;37(1):49-65. Epub 2009 Dec 23.
Evaluation of different tests based on observations for external model
evaluation of population analyses.
Brendel K, Comets E, Laffont C, Mentré F.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2874574/
The npde library for R:
Computing normalised prediction distribution errors to evaluate nonlinear
mixed-effect models: the npde add-on package for R.
Comets E, Brendel K, Mentré F.
Comput Methods Programs Biomed. 2008 May;90(2):154-66. Epub 2008 Jan 22.
http://www.hal.inserm.fr/inserm-00274332/en/
And I leave you with a very fitting question in the light of Jean's response :-)
Why Should Prediction Discrepancies Be Renamed Standardized Visual Predictive
Check?
Comets E, Brendel K, Mentré F.
J Clin Pharmacol. 2011 Sep 10. [Epub ahead of print]
http://www.hal.inserm.fr/inserm-00627625/fr/
All the best,
Emmanuelle
-------- Message original --------
Sujet: [NMusers] RE: VPC for non-uniform sampling
Date : Tue, 10 Jan 2012 22:30:59 +0000
De : Lavigne, Jean <[email protected]>
Pour : Ayyappa Chaturvedula <[email protected]>,
"[email protected]" <[email protected]>
Dear Ayyappa,
You may consider using "Standardized Visual Predictive Check" published in the
following links:
http://jcp.sagepub.com/content/52/1/39
http://www.page-meeting.org/pdf_assets/4050-Standardized%20Visual%20Predictive%20Check%20in%20Model%20Evaluation%20-%20PAGE2009%20submit.pdf
Best regards,
Jean
*From:*[email protected] [mailto:[email protected]] *On
Behalf Of *Ayyappa Chaturvedula
*Sent:* Tuesday, January 10, 2012 5:11 PM
*To:* [email protected]
*Subject:* [NMusers] VPC for non-uniform sampling
Dear expert users,
I am working on a dataset where subjects were sampled at different visits at
random. I have developed a model for the data but not sure how to do a VPC as
they do not have the same sampling scheme. I appreciate some guidance in this.
Regards,
Ayyappa
This electronic transmission may contain confidential and/or proprietary
information and is intended to be for the use of the individual or entity named
above. If you are not the intended recipient, be aware that any disclosure,
copying, distribution or use of the contents of this electronic transmission is
prohibited. If you have received this electronic transmission in error, please
destroy it and immediately notify us of the error. Thank you.
Dear Ayappa,
If you would like to write your own code for diagnostic plots, here is the R
code for SVPC.
http://jcp.sagepub.com/content/suppl/2011/02/22/0091270010390040.DC1/DS_10.1177_0091270010390040.pdf
As for the question Emmanuelle raised at the end of the email, please see
"Author's Response", J Clin Pharmacol. 2011 Dec 13. [Epub ahead of print]
http://jcp.sagepub.com/content/early/2011/12/12/0091270011427555.full.pdf+html
Best Regards,
Diane
Diane D. Wang, Ph.D.
Clinical Pharmcology
Oncology Business Unit
Pfizer La Jolla
10555 Science Center Dr. (CB10/1719)
San Diego, CA 92121
Office Phone: (858) 622-8021
Cell Phone: (858) 761-3667
Quoted reply history
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Emmanuelle Comets
Sent: Thursday, January 12, 2012 12:32 AM
To: [email protected]
Subject: Fwd: [NMusers] RE: VPC for non-uniform sampling
Dear Ayappa,
"Standardized Visual Predictive Check" are in fact prediction discrepancies
which were developed by Mentré et al., with a decorrelated version called
normalised prediction distribution errors (npde) in Brendel et al. npde are
available in Nonmem or Monolix. There is also a library in R to help compute
both pd and npde (the library is called npde).
Here are a few links to the original publications.
Prediction discrepancies and their evaluation:
Prediction discrepancies for the evaluation of nonlinear mixed-effects models.
Mentré F, Escolano S.
J Pharmacokinet Pharmacodyn. 2006 Jun;33(3):345-67. Epub 2005 Nov 13.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1989778/
The development of npde:
Metrics for external model evaluation with an application to the population
pharmacokinetics of gliclazide.
Brendel K, Comets E, Laffont C, Laveille C, Mentré F.
Pharm Res. 2006 Sep;23(9):2036-49. Epub 2006 Aug 12.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2124466/
An evaluation of the npde:
J Pharmacokinet Pharmacodyn. 2010 Feb;37(1):49-65. Epub 2009 Dec 23.
Evaluation of different tests based on observations for external model
evaluation of population analyses.
Brendel K, Comets E, Laffont C, Mentré F.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2874574/
The npde library for R:
Computing normalised prediction distribution errors to evaluate nonlinear
mixed-effect models: the npde add-on package for R.
Comets E, Brendel K, Mentré F.
Comput Methods Programs Biomed. 2008 May;90(2):154-66. Epub 2008 Jan 22.
http://www.hal.inserm.fr/inserm-00274332/en/
And I leave you with a very fitting question in the light of Jean's response :-)
Why Should Prediction Discrepancies Be Renamed Standardized Visual Predictive
Check?
Comets E, Brendel K, Mentré F.
J Clin Pharmacol. 2011 Sep 10. [Epub ahead of print]
http://www.hal.inserm.fr/inserm-00627625/fr/
All the best,
Emmanuelle
-------- Message original --------
Sujet: [NMusers] RE: VPC for non-uniform sampling
Date : Tue, 10 Jan 2012 22:30:59 +0000
De : Lavigne, Jean <[email protected]>
Pour : Ayyappa Chaturvedula <[email protected]>,
"[email protected]" <[email protected]>
Dear Ayyappa,
You may consider using "Standardized Visual Predictive Check" published in the
following links:
http://jcp.sagepub.com/content/52/1/39
http://www.page-meeting.org/pdf_assets/4050-Standardized%20Visual%20Predictive%20Check%20in%20Model%20Evaluation%20-%20PAGE2009%20submit.pdf
Best regards,
Jean
*From:*[email protected] [mailto:[email protected]] *On
Behalf Of *Ayyappa Chaturvedula
*Sent:* Tuesday, January 10, 2012 5:11 PM
*To:* [email protected]
*Subject:* [NMusers] VPC for non-uniform sampling
Dear expert users,
I am working on a dataset where subjects were sampled at different visits at
random. I have developed a model for the data but not sure how to do a VPC as
they do not have the same sampling scheme. I appreciate some guidance in this.
Regards,
Ayyappa
This electronic transmission may contain confidential and/or proprietary
information and is intended to be for the use of the individual or entity named
above. If you are not the intended recipient, be aware that any disclosure,
copying, distribution or use of the contents of this electronic transmission is
prohibited. If you have received this electronic transmission in error, please
destroy it and immediately notify us of the error. Thank you.
Dear Markus,
1. Here is the publication info. Diane D. Wang and Shuzhong Zhang,
"Standardized Visual Predictive Check Versus Visual Predictive Check for Model
Evaluation", J Clin Pharmacol January 2012 52: 39-54, first published on
January 21, 2011 doi:10.1177/0091270010390040
The R code is in the supplemental material which is only available online. I
was trying be send the R code out as an attachment in my last email but nmuser
does not take attachment. Below is the R code and I will also send you a copy
of the article and R code in a separate email. Please note that the editor
used a wrong version of the manuscript for publication (a wrong example was
used for simulation study 5). This will be addressed in the future.
########## A sample R code for conducting SVPC
#------- Define functions
percentile = function (x){
percent = quantile (x, probs = c(0.05, 0.5, 0.95), na.rm = T)
}
percent.matrix = function (x){
p = NULL
for (i in 1:length(x)){
p = rbind(p, x[[i]])
}
return (p)
}
SVPC = function(ori, sim){
n.of.sim = length(sim$ID)/length(ori$ID)
sim$OBS = rep(ori$DV, n.of.sim)
sim$IND = rep(0, length(sim$ID))
sim[sim$DV <= sim$OBS, "IND"] = 1
rank = tapply(sim$IND, list(sim$ID, sim$TIME), sum)
Pij = c()
for (i in 1:length(rank[,1])){
Pij = c(Pij, rank[i, ])
}
return (Pij/n.of.sim)
}
#---Import original nonmem PK dataset (pk.csv) and simulated PK dataset
(simu.tab)
#---Reorganize both datasets so that they only contain event records that are
relevant
#---for SVPC (for example: removing all dosing records)
#---Both datasets should have "ID", "TIME" and "DV" column variables defined as
such
setwd("c:/...")
ori.data = read.table(file="pk.csv", header=T, sep=",")
ori = ori.data[ori.data$AMT==0, c("ID", "TIME", "DV")]
sim.data = read.table(file="pk_svpc.tab", sep="")
name=names(ori.data)
names(sim.data) = name
sim = sim.data[sim.data$AMT== 0, c("ID", "TIME", "DV")]
PIJ = SVPC(ori, sim)
D.PIJ <- data.frame(TIME=names(PIJ), PIJ=PIJ)
D.PIJ$TIME <- as.numeric(as.character(D.PIJ$TIME))
D.PIJ$ID <- rep(unique(ori$ID), each=nrow(D.PIJ)/length(unique(ori$ID)))
D.PIJ <-D.PIJ[order(D.PIJ$ID, D.PIJ$TIME), ]
ori<-merge(ori, D.PIJ, by = intersect(names(ori), names(D.PIJ)), all=T)
ori <- ori[!is.na(ori$DV), ] #nrow(ori)
#----calculate percentiles of the observed data. If data at each timepoint are
not
#----rich enough, it is suggested not to calculate and plot the observed
percentiles.
percent.obs = tapply(ori$PIJ, ori$TIME, percentile)
percent.obs.time = as.numeric(names(percent.obs))
percent.obs.matrix = percent.matrix(percent.obs)
plot(ori$TIME, ori$PIJ, type="n", xlab = "Time", ylab = "Pij", cex=1)
title(main= "SVPC Plot")
points (ori$TIME, ori$PIJ, cex=1, col=1)
abline(h = 0.05, col=1, lwd = 1)
abline(h = 0.95, col=1, lwd = 1)
abline(h = 0.5, col=1, lwd = 1)
#----plot 5, 50, and 95th percentiles of the observed data, it is optional
for (i in 1:3){
lines(percent.obs.time, percent.obs.matrix[,i], lwd = 1, col=2)
}
Thanks,
Diane
Diane D. Wang, Ph.D.
Clinical Pharmcology
Oncology Business Unit
Pfizer La Jolla
10555 Science Center Dr. (CB10/1719)
San Diego, CA 92121
Office Phone: (858) 622-8021
Cell Phone: (858) 761-3667
Quoted reply history
From: markus joerger [mailto:[email protected]]
Sent: Saturday, January 14, 2012 1:48 AM
To: Wang, Diane
Subject: Re: [NMusers] RE: VPC for non-uniform sampling
dear Diane Wang,
I would be interested on the R-Code for SVPC; your reference for the J Clin
Pharmacology needs "log-in"; so reference with author-number etc. would be
preferable!
thanks and regards,
Markus
2012/1/13 Wang, Diane <[email protected]>
Dear Ayappa,
If you would like to write your own code for diagnostic plots, here is the R
code for SVPC.
http://jcp.sagepub.com/content/suppl/2011/02/22/0091270010390040.DC1/DS_10.1177_0091270010390040.pdf
As for the question Emmanuelle raised at the end of the email, please see
"Author's Response", J Clin Pharmacol. 2011 Dec 13. [Epub ahead of print]
http://jcp.sagepub.com/content/early/2011/12/12/0091270011427555.full.pdf+html
Best Regards,
Diane
Diane D. Wang, Ph.D.
Clinical Pharmcology
Oncology Business Unit
Pfizer La Jolla
10555 Science Center Dr. (CB10/1719)
San Diego, CA 92121
Office Phone: (858) 622-8021 <tel:%28858%29%20622-8021>
Cell Phone: (858) 761-3667 <tel:%28858%29%20761-3667>
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Emmanuelle Comets
Sent: Thursday, January 12, 2012 12:32 AM
To: [email protected]
Subject: Fwd: [NMusers] RE: VPC for non-uniform sampling
Dear Ayappa,
"Standardized Visual Predictive Check" are in fact prediction discrepancies
which were developed by Mentré et al., with a decorrelated version called
normalised prediction distribution errors (npde) in Brendel et al. npde are
available in Nonmem or Monolix. There is also a library in R to help compute
both pd and npde (the library is called npde).
Here are a few links to the original publications.
Prediction discrepancies and their evaluation:
Prediction discrepancies for the evaluation of nonlinear mixed-effects models.
Mentré F, Escolano S.
J Pharmacokinet Pharmacodyn. 2006 Jun;33(3):345-67. Epub 2005 Nov 13.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1989778/
The development of npde:
Metrics for external model evaluation with an application to the population
pharmacokinetics of gliclazide.
Brendel K, Comets E, Laffont C, Laveille C, Mentré F.
Pharm Res. 2006 Sep;23(9):2036-49. Epub 2006 Aug 12.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2124466/
An evaluation of the npde:
J Pharmacokinet Pharmacodyn. 2010 Feb;37(1):49-65. Epub 2009 Dec 23.
Evaluation of different tests based on observations for external model
evaluation of population analyses.
Brendel K, Comets E, Laffont C, Mentré F.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2874574/
The npde library for R:
Computing normalised prediction distribution errors to evaluate nonlinear
mixed-effect models: the npde add-on package for R.
Comets E, Brendel K, Mentré F.
Comput Methods Programs Biomed. 2008 May;90(2):154-66. Epub 2008 Jan 22.
http://www.hal.inserm.fr/inserm-00274332/en/
And I leave you with a very fitting question in the light of Jean's response :-)
Why Should Prediction Discrepancies Be Renamed Standardized Visual Predictive
Check?
Comets E, Brendel K, Mentré F.
J Clin Pharmacol. 2011 Sep 10. [Epub ahead of print]
http://www.hal.inserm.fr/inserm-00627625/fr/
All the best,
Emmanuelle
-------- Message original --------
Sujet: [NMusers] RE: VPC for non-uniform sampling
Date : Tue, 10 Jan 2012 22:30:59 +0000
De : Lavigne, Jean <[email protected]>
Pour : Ayyappa Chaturvedula <[email protected]>,
"[email protected]" <[email protected]>
Dear Ayyappa,
You may consider using "Standardized Visual Predictive Check" published in the
following links:
http://jcp.sagepub.com/content/52/1/39
http://www.page-meeting.org/pdf_assets/4050-Standardized%20Visual%20Predictive%20Check%20in%20Model%20Evaluation%20-%20PAGE2009%20submit.pdf
Best regards,
Jean
*From:*[email protected] [mailto:[email protected]] *On
Behalf Of *Ayyappa Chaturvedula
*Sent:* Tuesday, January 10, 2012 5:11 PM
*To:* [email protected]
*Subject:* [NMusers] VPC for non-uniform sampling
Dear expert users,
I am working on a dataset where subjects were sampled at different visits at
random. I have developed a model for the data but not sure how to do a VPC as
they do not have the same sampling scheme. I appreciate some guidance in this.
Regards,
Ayyappa
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