RE: Confidence intervals of PsN bootstrap output
Hi Matt,
OK, I can certainly see that transformations will be helpful in
bootstrapping; for those persons that throw away samples with
unsuccessful termination or cov step. They would otherwise discard all
bootstrap estimates that indicate Emax is close to zero. Since I most
often use all bootstrap samples that terminate at a minimum I guess in
practice I would virtually have the same distribution of Emax,
regardless of transformation or not?
I fully agree transformations are useful to get convergence and
successful covstep on the original dataset (and I tend to keep the same
transformation when bootstrapping, but only for simplicity). However, I
sometimes use the bootstrap results to which parameters should be
transformed in the first place. From what I have seen, bootstrapping the
transformed model again has never changed the (non-parametric bootstrap)
distribution when boundaries were the same (e.g. both models bound to
positive values of Emax).
Cheers
Jakob
Quoted reply history
-----Original Message-----
From: Matt Hutmacher [mailto:[email protected]]
Sent: 11 July 2011 17:39
To: Ribbing, Jakob; 'nmusers'
Subject: RE: [NMusers] Confidence intervals of PsN bootstrap output
Hi Jakob,
"The 15% bootstrap samples where data suggest a negative drug effect
would
in one case terminate at the zero boundary, in the other case it would
terminate (often unsuccessfully) at highly negative values for log
Emax"...
I have seen that transformation can make the likelihood surface more
stable.
In my experience, when runs terminate using ordinary Emax
parameterization
with 0 lower bounds (note that NONMEM is using a transformation behind
the
scenes to avoid constrained optimization), you can avoid termination and
even get the $COV to run with different parameterizations. The estimate
might be quite negative as you suggest, but I have seen it recovered.
Also,
I have seen termination avoided and COV achieved with Emax=EXP(THETA(X))
and
EC50=EXP(THETA(Y)) when EC50 and EMAX becomes large. I have seen
variance
components that can be estimated in this way but not with traditional
$OMEGA
implementation.
Best,
matt