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RE: VPC appropriateness in complex PK

From: Wang, Diane <Diane.Wang>
Date: Mon, 21 Sep 2009 18:57:26 -0400

Nick and Martin,


Thank you for pointing out similarity between SVPC and PDE method =
published by Mentré and Escolano. The end result of these two =
approaches is the same but the simulation process is a little different =
as they were developed independently and from a different perspective. =
In SVPC, we simulated 1000 (100 is actually enough as shown in my =
presentation)individual's concentrations (including both between and =
within subject variability) for each individual based on its own study =
template to computer percentile of each observation. This allows us to =
fix all the covariate effects including dose to evaluate random effect =
and structure model. The paper by M Mentré et al simulated 1000 or =
more individual PK parameters (only between subject variability) and =
used the normal cumulative distribution function (within subject =
variability) to computer the percentile. Mentre's paper focused on =
statistics of predictive discrepancy and was not discussed in the =
context of VPC. PDE is not proposed as a solution or an alternative =
approach for VPC when VPC is not feasible or can not be performed =
correctly. This might be why no one has used it for the purpose of =
predictive check since its publication. In a way, SVPC can be viewed as =
an application of PDE although the simulation process is easier. One can =
simply use the original dataset as simulation dataset and set =


The main purpose of my PAGE presentation is to raise awareness of the =
inadequacy of VPC in many situations. Among published population PK/PD =
papers, VPC was often conducted regardless of presence of covariate =
effect, individualized dosing and other fixed effects. As to which =
approach to use, as long as it is conducted correctly and fit the =
purpose, it is an individual's choice. It is always good to have =







From: owner-nmusers
On Behalf Of Martin Bergstrand
Sent: Monday, September 21, 2009 9:16 AM
To: 'nmusers'
Subject: FW: [NMusers] VPC appropriateness in complex PK


Dear NMusers,

For some reason my last message to NMusers got lost in www-space. Since =
both Leonid and Nick have responded to my initial message I repost this =
message so that you can follow the discussion (see email below).

In addition to this message I would also like to comment on the messages =
by Diane, Leonid and Nick.

Nick and Leonid: I agree that it would be useful if one could also =
simulate that adaptive design (e.g. dose adaptations) and show the =
observations on the non transformed scale. However this will in many =
cases be very hard since dos adaptations are often done not according =
to a strict algorithm and/or all information supporting the dose =
alterations is not available. It is to my experience quite commonly =
written I study protocols that dose adjustments can be done "by the =
discretion of the investigator".

Diane and Leonid: If I understood the SVPC procedure correctly from =
Diane's presentation it utilizes a principle similar to that behind =
Numerical Predictive Check (NPC). Most of all SVPC seem to have a =
striking similarity to the first version of the prediction discrepancies =
as described by Metré et al (1). The prediction discrepancies have =
been further developed into the normalised prediction distribution =
errors (NPDE) (2). From my experience both NPC and NPDE are useful =
diagnostic tools but not applicable to data from studies with adaptive =
dos adjustments (correlation between ETAs and design). What is the =
unique feature with SVPC that sets it apart from the prediction =
discrepancies and makes it applicable to studies with adaptive dos =

Nick: Regarding this sentence "The empirical PRED-corrected VPC does not =
give this kind of support for future use of the PK model under an =
adaptive design scenario". Why is this? If the PC-VPC can verify that =
you have an acceptable structure model and unbiased parameter estimates =
you can then simulate any type of adaptive design scenario.

Best regards,


1. Prediction discrepancies for the evaluation of ... Mentré F, =
Escolano S. JPKPD. 2006

2. Computing normalised prediction distribution errors ... Comets =
E, Brendel K, Mentré F. CMPB. 2008

From: Martin Bergstrand [mailto:martin.bergstrand
Sent: den 20 september 2009 19:32
To: 'Leonid Gibiansky'; 'Nick Holford'
Cc: 'Dider Heine'; 'nmusers
Subject: RE: [NMusers] VPC appropriateness in complex PK

Dear Leonid and Nick,

You have both written that there is no simulation based diagnostic that =
can be applied in the case of adaptive designs (unless you can simulate =
the adaptations). Below I will try to describe why I think that PC-VPCs =
can be used under these circumstances.

The example that Leonid describe is very similar to one of the example =
in the abstract about PC-VPCs that I referred to previously (see example =
3). With this example we demonstrate that PC-VPCs can be used in the =
presence of adaptive designs such as TDM. The prediction corrected =
dependent variable in a PC-VPC is unaffected by changes in independent =
variables included in the model such as dose and covariate effects. It =
can be seen as if the median in a PC-VPC represent a typical individual =
with a typical dose and a typical set of covariates. If we look at a =
prediction interval for a PC-VPC that represent only the variability =
that is explained by random effects in the model and nothing that comes =
from fixed effects (dose, covariates and time). For this reason PC-VPCs =
can be used also in the cases when we do not know the exact algorithm =
for the adaptations made (e.g. dose adjustments). In a very simple case =
where we have linear kinetics, no covariates in the model and no binning =
across the independent variable on the x-axis (e.g. time) PRED =
correction will be the same a dose normalization of both the observed =
and simulated data. However the PRED correction can be more universally =
applied than a dose normalization. PRED correction does not handle all =
types of adaptive designs that you could think of. For instance

The above described feature of PC-VPCs are one of reasons I find it =
useful. In the cases with adaptive designs PC-VPCs will in my mind =
replace traditional VPCs whereas in many other cases it will only be a =
complement to stratified VPCs to better diagnose the random effect =
components of a model.

More about this can be read in the ACoP abstract:

Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. Prediction corrected

visual predictive checks ACOP. =
2009 <> .

Ps. PRED correction does not handle all types of adaptive designs that =
you could think of. For instance adaptive censoring of data (i.e. study =
discontinuation) will not be this easily handled.

Kind regards,


Received on Mon Sep 21 2009 - 18:57:26 EDT

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