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RE: Fwd: Should we generate VPCs with or without uncertainty?

From: Stefano Zamuner <stefano.6.zamuner>
Date: Mon, 8 Jun 2015 17:24:01 +0000

Hi Devin and Matts,

Of interest, we had similar internal discussion within our department about this topic quite recently.

I would agree with Devin that it is preferable to include parameters uncertainty in a VPC, especially if they are used for relevant decision making. It may result on large CI around your 5th, median and 95th percentiles, however it means that you likely have limited information to estimate some of the variability terms. Don’t forget that any “standard VPC” still provides some uncertainty around your percentiles based on random effects and sample size.

There are several ways to embed params uncertainty, I found quite useful to use the covariance-matrix (from NM) to embed it in a simulation script – it is quite straightforward to do that in R with some coding effort (I think Pirana has R code for that as well), but I’m sure it can be easily replicated with different tools available.

I guess it would be interesting to see how pharmacometricians would derive the parameters uncertainty: i.e, covariance matrix vs bootstrap or other means. I recently saw an elegant and efficient solution of bootstrap VPC done from a colleague of mine.


Stefano Zamuner PhD
Senior Director Clinical Pharmacology


From: owner-nmusers .com [mailto:owner-nmusers
Sent: 08 June 2015 17:30
To: Matts Kågedal; nmusers
Subject: Re: [NMusers] Fwd: Should we generate VPCs with or without uncertainty?


The way I see the CI's around the point estimates provided in the VPC can help provide a useful indication of model robustness, especially in regards to the impact of random effects components, in that portion of your model. Especially for heterogeneous data (or even all rich data for that matter) there are a number of binning strategies that can be used, which can impact the aforementioned intervals.

At the end of the day, we must use our judgement for how the model is being used to support decisions, and whether information regarding uncertainty can provide additional support towards the overall evaluation of the key questions you are trying to address. Eg, if you are dealing with a narrow therapeutic index drug the value of having a 'feel' for the robustness of the ability of your model to describe the tails may be valuable information, even as a qualitative indication of model robustness. On the other hand, if you are trying to make a decision regarding dose adjustment between different populations and are looking to normalize large differences, as well as are constrained to certain oral dosage options, uncertainty in the point estimates will likely provide very little support to an argument one way or the other.

Finally, in my opinion, inclusion/exclusion also relies on what the plot is trying to communicate. Are you trying to personally evaluate model adequacy, sure, but if using to convey to non- modelers/quantitative people that your model describes the data - include a visualization of uncertainty at your own peril :-)

So, for better or worse, I would say - it depends, though I would be highly concerned if major decisions rode on inclusion/exclusion of parameter uncertainty, in most cases.

Devin Pastoor
Center for Translational Medicine
University of Maryland, Baltimore

On Mon, Jun 8, 2015 at 11:57 AM Matts Kågedal <mattskagedal
Hi all,

Creation of VPCs is a way to assess if simulated data generated by the model is compatible with observed data.
VPCs are usually based on parameter point estimates of the model. Sometimes parameter uncertainty is also accounted for in the generation of VPCs (PPCs) where each simulated replicate of the data set is based on a new set of parameter values representing the uncertainty of the estimates (e.g. based on a bootstrap).

I wonder if inclusion of uncertainty in this way is really appropriate or if it just makes the confidence intervals wider and hence easier to qualify the model. Is it possible based on such an approach, that a model might look good, when in fact no likely combination of parameter values (based on parameter uncertainty) would generate data that are compatible with the observations?

To illustrate my question:
I could generate 100 sets of parameters reflecting parameter uncertainty (e.g. from a bootstrap). Based on each set of parameters I could then generate a separate VPC (e.g. showing median, 5 and 95% percentile) to see if any of the parameter sets are compatible with data. I would then have 100 VPCs, each based on a separate set of parameter values reflecting the parameter correlations and uncertainty.

If the VPC based on point estimates looks bad, I would (generally) expect that the other VPCs would be worse (they all have lower likelihood), so that we have 101 VPCs that does not look good. Some might over predict and some underpredict, some might describe parts of the relation better than the VPC based on the point estimates.

By putting the VPCs together from all parameter vectors, the CI becomes wider, and perhaps now includes the observed data. So based on a set of 100 parameter vectors which individually are not compatible with the observed data I have now generated a VPC (PPC) where the confidence interval actually includes the observed metric (e.g median). It seems to me that based on such an approach it is possible that a model might look good, when in fact no likely individual set of parameter values would generate data that are compatible with the observations.

Simulation based on parameter uncertainty is useful when we want to make inference, but I am unsure of its use for model qualification. In any case it is confusing that we some times simulate based on point estimates and sometimes based on parameter uncertainty without any particular rationale as far as I understand.

Would be interested if someone could shed some light on the inclusion of uncertainty in simulations for model qualification (VPCs).

Best regards,
Matts Kagedal

Pharmacometrician, Genentech


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Received on Mon Jun 08 2015 - 13:24:01 EDT

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