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 > -----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 > > > > > >
