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Re: VPC appropriateness in complex PK

From: Dider Heine <ddrheine>
Date: Mon, 21 Sep 2009 16:31:27 -0700

Diane,
If you had read further, you would realize that Mentre published a follow-u=
p
article (Comets E, Brendel K, Mentré F. Computing normalised prediction
distribution errors to evaluate nonlinear mixed-effect models: The npde
add-on package for R. Comput Methods Programs Biomed. 2008;90(2):154-66)
which takes the SPC method one step further than the original publication
(and your re-presentation of that work) removing the correlation between
individual observations. Can we agree to stop calling it the re-named
version (SVPC)?

Cheers!
Dider



On 9/21/09, Wang, Diane <Diane.Wang
>
> Nick and Martin,
>
>
>
> Thank you for pointing out similarity between SVPC and PDE method publish=
ed
> 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 10=
00
> (100 is actually enough as shown in my presentation)individual’s
> concentrations (including both between and within subject variability) fo=
r
> each individual based on its own study template to computer percentile of
> each observation. This allows us to fix all the covariate effects includ=
ing
> dose to evaluate random effect and structure model. The paper by M Mentr=
é 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 focuse=
d 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 VP=
C
> when VPC is not feasible or can not be performed correctly. This might b=
e
> 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 altho=
ugh
> 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 effec=
t,
> individualized dosing and other fixed effects. As to which approach to u=
se,
> 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
>
>
>
>
>
>
> ------------------------------
>
> *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 case=
s
> be very hard since dos adaptations are often done not according to a str=
ict
> algorithm and/or all information supporting the dose alterations is not a=
vailable.
> 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 Num=
erical
> 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 (NPD=
E)
> (2). From my experience both NPC and NPDE are useful diagnostic tools but=
 not
> applicable to data from studies with adaptive dos adjustments (correlatio=
n
> between ETAs and design). What is the unique feature with SVPC that sets =
it
> apart from the prediction discrepancies and makes it applicable to studie=
s
> 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 adaptiv=
e
> design scenario”. Why is this? If the PC-VPC can verify that you have a=
n
> 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:martin.bergstrand
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 c=
an
> 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 b=
e
> 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 th=
e
> 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 tim=
e).
> 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 mode=
l
> 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 observe=
d
> and simulated data. However the PRED correction can be more universally
> applied than a dose normalization. PRED correction does not handle all ty=
pes
> of adaptive designs that you could think of. For instance
>
> The above described feature of PC-VPCs are one of reasons I find it usefu=
l.
> 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 mode=
l.
>
> *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 yo=
u
> could think of. For instance adaptive censoring of data (i.e. study
> discontinuation) will not be this easily handled.
>
> Kind regards,
>
> Martin
>

Received on Mon Sep 21 2009 - 19:31:27 EDT

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