Ayyappa,
It is possible to do some testing of demographic covariates using ANOVA
-- but this requires the two stage population approach i.e. obtain
individual parameter estimates first then apply ANOVA as though the
parameters were observations. The standard two stage method is known to
produce biased estimates of the variances because the true between
subject variability is confounded with the individual parameter
estimation error (Sheiner 1984). There are fancier two stage methods
that can account for this but they are somewhat complicated. All two
stage methods require that there is sufficient data per individual to
estimate all parameters of interest. While this is desirable from a
design viewpoint, even for a full population analysis, the reality is
that PKPD studies are usually sub-optimally designed and individuals may
not have enough observations. The two stage approach cannot deal with
this but the full population approach can.
The full population approach allows you to simultaneously estimate the
relationship between the parameter of interest eg EC50 and the covariate
e.g. age on a continuous scale. This means you get a more realistic
estimate fo parameter uncertainty because you are not making the
assumption that the EC50 values are estimated without error e.g. bias
arising from not understanding the covariate relationship. In addition,
covariate relationships can be non-linear. ANOVA cannot handle
non-linear covariate relationships as far as I know. Thus the population
approach is more honest and more flexible than ANOVA.
Finally as Sir Michael Rawlins (Chairman of the NICE in the UK) pointed
out yesterday the traditional statistical approach to clinical trials
does not adequately describe the clinical pharmacology and benefits of
medicines. The flexibility of the population approach allows it to used
for 'learning' as well as 'confirming' (Sheiner 1997). This combination
of approaches is in keeping with the broader philosophy posed by Rawlins.
http://www.politics.co.uk/opinion-formers/press-releases/royal-college-physicians-sir-michael-rawlins-attacks-traditional-ways-assessing-evidence-$1245035$365674.htm
Sheiner LB. The population approach to pharmacokinetic data analysis:
rationale and standard data analysis methods. Drug Metab Rev.
1984;15(1-2):153-71.
Sheiner LB. Learning versus confirming in clinical drug development.
Clinical Pharmacology & Therapeutics. 1997;61(3):275-91.
[EMAIL PROTECTED] wrote:
Nick,
May be I did not put forth the question right. Let me try again, I
want to know the advantage of analyzing the pooled data from different
clinical studies to understand the demographic differences by ANOVA vs
finding a demographic covariate tested by NONMEM.
Regards,
Ayyappa Chaturvedula
GlaxoSmithKline
1500 Littleton Road,
Parsippany, NJ 07054
Ph:9738892200
*"Nick Holford" <[EMAIL PROTECTED]>*
Sent by: [EMAIL PROTECTED]
16-Oct-2008 14:38
To
[email protected]
cc
Subject
Re: [NMusers] Why population PK approach is good for pooling data
compared to Classical statistical analysis?
Ayyappa,
As far as I know any 'classical statistical analysis' that one can do
using regression can be done with NONMEM. It is also possible to do
hypothesis testing on means (t-test, ANOVA), logistic regression and
survival analysis.
What kinds of 'classical statistical analysis' do you want to do that
you cannot do with NONMEM?
Nick
[EMAIL PROTECTED] wrote:
>
> Dear Group,
>
> Could anybody explain or direct me to some literature why population
> PK approach allows us to pool data from different studies but no
> classical statistical analysis?
>
> Regards,
> Ayyappa Chaturvedula
> GlaxoSmithKline
> 1500 Littleton Road,
> Parsippany, NJ 07054
> Ph:9738892200
--
Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New
Zealand
[EMAIL PROTECTED] tel:+64(9)923-6730 fax:+64(9)373-7090
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
--
Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
[EMAIL PROTECTED] tel:+64(9)923-6730 fax:+64(9)373-7090
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
