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RE: General question on modeling

From: michael.looby
Date: Tue, 20 Mar 2007 13:45:40 +0100

Dear All

Certainly an interesting discussion. While developing a model of the
relationship between the continuous values of a covariate and a response
is of benefit in terms of characterising the dependency, it is not a given
that dosing on a continuous scale adds value in terms of better therapy.
The key to determining the number of steps in a covariate based dosage
algorithm will be the amount of variability accounted for by the
covariate. Thus, the greater the amount of variability accounted, the
smaller the number of necessary steps. To picture this think of the
extremes: if the covariate accounts for all the variability then
continuous adjustment will be optimal and at the other (absurd) extreme
the covariate does not account for any variability then no adjustment will
be best. I mention the latter because very often most covariates tested
account for very little variability despite the huge effort put into
testing them.
From my perspective adding covariates only adds benefit if they reduce
model bias and/or explain enough variability to have benefit for the
purpose of individualisation. These thoughts should be central to those
involved in this activity

Kind regards
Mick





Mark Sale - Next Level Solutions <mark
Sent by: owner-nmusers
20.03.2007 11:29

 
        To:
        cc: nmusers
        Subject: RE: [NMusers] General question on modeling


Mark,
  Wow, are we getting off the original subject (which we always do).
  I'd suggest that oncologists and epileptolgist are exceptions - they
have learned to deal with individualized dosing because of the toxicity
of the drug they use. Many, many studies have documented the issues of
mis-dosing drugs, and estimated the resulting fatalities. Making
dosing more complicated is unlikely the help. In addition, each
company very much wants their drug to be simpler to use than their
competitors.


Mark Sale MD
Next Level Solutions, LLC
www.NextLevelSolns.com


> -------- Original Message --------
> Subject: Re: [NMusers] General question on modeling
> From: Nick Holford <n.holford
> Date: Mon, March 19, 2007 9:36 pm
> To: nmusers
>
> Mark,
>
> > Reality is that the vast majority of providers couldn't
> > deal with renal function as a continuous variable in dosing. Writing
a
> > label requiring them to do so would not result in an optimal outcome.
>
> The vast majority of providers are perfectly able to deal with renal
function as a continuous variable. They don't do it because they dont
appreciate the mistakes they are encouraged to make by untested labelling
strategies.
>
> Clinical trials have shown clinicians can be encouraged to use
quantitative dosing on a continuous scale with a proven benefit in outcome
by ignoring the drug label advice e.g.
>
> Evans W, Relling M, Rodman J, Crom W, Boyett J, Pui C. Conventional
compared with individualized chemotherapy for childhood acute
lymphoblastic leukemia. New England Journal of Medicine 1998;338:499-505
>
> BTW I'm still waiting to hear if you have an example of finding the Holy
Grail...
>
> >
> > > -------- Original Message --------
> > > Subject: Re: [NMusers] General question on modeling
> > > From: Nick Holford <n.holford
> > > Date: Mon, March 19, 2007 8:27 pm
> > > To: nmusers
> > >
> > > Mark,
> > >
> > > If we are talking about science then we are not talking about
regulatory decision making. The criteria used for regulatory approval and
labelling are based on pragmatism not science e.g. using intention to
treat analysis (use effectiveness rather than method effectiveness),
dividing a continuous variable like renal function into two categories for
dose adjustment. This kind of pragmatism is more art than science because
it does not correctly describe the drug properties (ITT typically
underestimates the true effect size) nor rationally treat the patient with
extreme renal function values.
> > >
> > > As Steve reminded us all models are wrong. The issue is not whether
some ad hoc model building algorithm is correct or has the right type 1
error properties under some null that is largely irrelevant to the
purpose. The issue is does the model work well enough to satisfy its
purpose. Metrics of model performance should be used to decide if a model
is adequate not a string of dubiously applied P values.
> > >
> > > The search process is up to you. I think from your knowledge of
computer search methods you will appreciate that those methods that
involve more randomness/wild jumps in the algorithm generally have a
better chance of approaching a global minimum.
> > >
> > > IMHO the covariate search process is like the search for the Holy
Grail. Its fundamentally a process for those with a religious belief that
there is some special set of as yet unidentified covariates that will
explain between subject variability. As a non believer I think that all
the major leaps in explaining BSV comes from prior knowledge (weight,
renal function, drug interactions, genetic polymorphisms) and none have
been discovered by trying all the available covariates during a blind
search. If you have a counter example then please let me know and tell me
how much the BSV variance was reduced when this unsuspected covariate was
added to a model with appropriate prior knowledge covariates.
> > >
> > > Nick
> > >
> --
> Nick Holford, Dept Pharmacology & Clinical Pharmacology
> University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New
Zealand
> email:n.holford
> http://www.health.auckland.ac.nz/pharmacology/staff/nholford/




Received on Tue Mar 20 2007 - 08:45:40 EDT

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