RE: VPC appropriateness in complex PK
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
SUBPROBLEMS=100.
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 options.
Diane
Quoted reply history
________________________________
From: [email protected] [mailto:[email protected]] 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 adjustments?
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,
Martin
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:[email protected]]
Sent: den 20 september 2009 19:32
To: 'Leonid Gibiansky'; 'Nick Holford'
Cc: 'Dider Heine'; '[email protected]'; 'Wang, Diane'
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 http://www.go-acop.org/acop2009/posters ACOP. 2009
http://www.go-acop.org/acop2009/posters%20ACOP.%202009 .
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,
Martin