RE: Sparse (pediatric) and rich (adult) data
Marc
I agree that the use of a fully Bayesian analysis might be valueable here.
In option 2 when you suggest analysing the paediatric data - do you mean by
itself without the adult data or a combined analysis?
If the former then Aris and Leon (Manchester group) have done some work
comparing simultaneous analysis with sequential Bayesian analysis. I can't
recall where it was published - but it's worth a read.
Steve
--
Professor Stephen Duffull
Chair of Clinical Pharmacy
School of Pharmacy
University of Otago
PO Box 913 Dunedin
New Zealand
E: [EMAIL PROTECTED]
P: +64 3 479 5044
F: +64 3 479 7034
Design software: www.winpopt.com
Quoted reply history
> -----Original Message-----
> From: Gastonguay, Marc [mailto:[EMAIL PROTECTED]
> Sent: Saturday, 31 May 2008 2:20 a.m.
> To: Stephen Duffull
> Cc: 'Leonid Gibiansky'; 'Chandrasekhar Udata'; [email protected]
> Subject: Re: [NMusers] Sparse (pediatric) and rich (adult) data
>
> Steve - Thanks for making the point about the importance of
> experimental design. Often times when pooling adult and
> pediatric data, data are imbalanced, and pediatric PK designs
> are much less informative than the adult data. If, for a
> particular drug and disease state, pediatric patients really
> are just small adult patients, the design deficiency isn't
> much of a concern - but that's not always the case.
>
> Although very useful for scaling body-size related
> differences in PK parameters from adults to peds, the
> allometric "small adult" assumption, doesn't always provide
> the complete story. There are other bits of information about
> pediatric PK (e.g. developmental changes, pediatric disease
> state effects) that we'd like to learn about directly from
> the pediatric data.
>
> The analysis of the pooled data in this case (sparse,
> poorly-optimized pediatric data with more informative adult
> data) is similar to a Bayesian data analysis, with
> informative prior distributions for most/all model
> parameters. An alternative approach to analyzing the sparse
> pediatric data could be:
>
> 1. Assess the expected precision of PK parameters under the
> pediatric data alone, using a PFIM-type method.
> 2. Analyze the pediatric data, using a full Bayesian
> estimation method. Informative prior distributions based on
> adults would be selectively applied to those parameters with
> poor design support in the pediatric data alone, while other
> parameters which are of particular interest in the pediatric
> population could be estimated with diffuse prior distributions.
>
> This approach allows the pediatric data alone to influence
> the estimation of a subset of parameters (hopefully, those
> components you'd like to learn about), while relying on prior
> adult information to anchor some of the more poorly supported
> components of the model.
>
> Marc
>
> Marc R. Gastonguay, Ph.D.
> President & CEO, Metrum Research Group LLC [www.metrumrg.com]
> Scientific Director, Metrum Institute [www.metruminstitute.org]
> Direct: 860-670-0744 Main: 860-735-7043
> Email: [EMAIL PROTECTED]
>
>
>
>
> On May 28, 2008, at 9:49 PM, Stephen Duffull wrote:
>
>
> Leonid
>
>
>
> I hope that you do not dispute that in this
> particular case
>
>
> you need to use adult data (50 full profiles)
> rather than
>
>
> discard them and use only kids data (3 sample
> per subject, 20
>
>
> subjects)?
>
>
>
> I definitely do not dispute the need to have both adult
> and paediatric data
> in the analysis (so I agree :-) ). I see two reasons
> for this (perhaps more
> if I took more time). The first and most important
> reason is combining
> adult and paediatric data together is a great (only)
> way to learn how
> children differ pharmacokinetically from adults and how
> doses can be scaled
> to achieve equivalent exposures. Secondly, especially
> in this case, it is
> often helpful to combine data sets together to improve
> the informativeness
> of the overall design. This latter point, however was
> the point of my
> previous email. Some care must be taken to assess the
> accuracy of covariate
> effects given the unbalanced nature of the design.
>
>
>
> While optimal design can be used to extract more
>
>
> information from the same number of samples, it
> is not a
>
>
> substitute for the real data. Even with optimal
> design of the
>
>
> pediatric study (with the same 20 subjects, 3
> optimal sample
>
>
> points) I bet you would gain by using adult
> data as well.
>
>
>
> You always gain by summing over data (unless the new
> data is negatively
> informative which is unlikely in any PK situation). So
> I don't exactly
> follow your point. The question to me is simply, what
> chance do I have of
> identifying a model that allows me to draw
> appropriately accurate
> conclusions. Optimal design is a way that allows
> investigators to improve
> the informativeness of data. Obviously, no data = no
> information.
>
> Steve
> --
> Professor Stephen Duffull
> Chair of Clinical Pharmacy
> School of Pharmacy
> University of Otago
> PO Box 913 Dunedin
> New Zealand
> E: [EMAIL PROTECTED]
> P: +64 3 479 5044
> F: +64 3 479 7034
>
> Design software: www.winpopt.com
>
>
>
>
>
>